.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "user-guide/vision/auto_quickstarts/plot_classification_tutorial.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_user-guide_vision_auto_quickstarts_plot_classification_tutorial.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_user-guide_vision_auto_quickstarts_plot_classification_tutorial.py:


.. _vision_classification_tutorial:

==============================================
Classification Model Validation Tutorial
==============================================

In this tutorial, you will learn how to validate your **classification model** using deepchecks test suites.
You can read more about the different checks and suites for computer vision use cases at the
:doc:`examples section  </checks_gallery/vision>`.

A classification model is usually used to classify an image into one of a number of classes. Although there are
multi label use-cases, in which the model is used to classify an image into multiple classes, most use-cases
require the model to classify images into a single class.
Currently deepchecks supports only single label classification (either binary or multi-class).

.. code-block:: bash

    # Before we start, if you don't have deepchecks vision package installed yet, run:
    import sys
    !{sys.executable} -m pip install "deepchecks[vision]" --quiet --upgrade # --user

    # or install using pip from your python environment

.. GENERATED FROM PYTHON SOURCE LINES 27-29

Defining the data and model
===========================

.. GENERATED FROM PYTHON SOURCE LINES 29-49

.. code-block:: default


    # Importing the required packages

    import os
    import urllib.request
    import zipfile

    import albumentations as A
    import matplotlib.pyplot as plt
    import numpy as np
    import PIL.Image
    import torch
    import torchvision
    from albumentations.pytorch import ToTensorV2
    from torch import nn
    from torchvision import transforms
    from torchvision.datasets import ImageFolder

    from deepchecks.vision.classification_data import ClassificationData








.. GENERATED FROM PYTHON SOURCE LINES 50-53

Downloading the dataset
~~~~~~~~~~~~~~~~~~~~~~~
The data is available from the torch library. We will download and extract it to the current directory.

.. GENERATED FROM PYTHON SOURCE LINES 53-59

.. code-block:: default


    url = 'https://download.pytorch.org/tutorial/hymenoptera_data.zip'
    urllib.request.urlretrieve(url, 'hymenoptera_data.zip')

    with zipfile.ZipFile('hymenoptera_data.zip', 'r') as zip_ref:
        zip_ref.extractall('.')







.. GENERATED FROM PYTHON SOURCE LINES 60-68

Load Data
~~~~~~~~~

We will use torchvision and torch.utils.data packages for loading the data.
The model we are building will learn to classify **ants** and **bees**.
We have about 120 training images each for ants and bees.
There are 75 validation images for each class.
This dataset is a very small subset of imagenet.

.. GENERATED FROM PYTHON SOURCE LINES 68-142

.. code-block:: default


    class AntsBeesDataset(ImageFolder):
        def __init__(self, *args, **kwargs):
            """
            Overrides initialization method to replace default loader with OpenCV loader
            :param args:
            :param kwargs:
            """
            super(AntsBeesDataset, self).__init__(*args, **kwargs)

        def __getitem__(self, index: int):
            """
            overrides __getitem__ to be compatible to albumentations
            Args:
                index (int): Index
            Returns:
                tuple: (sample, target) where target is class_index of the target class.
            """
            path, target = self.samples[index]
            sample = self.loader(path)
            sample = self.get_cv2_image(sample)
            if self.transforms is not None:
                transformed = self.transforms(image=sample, target=target)
                sample, target = transformed["image"], transformed["target"]
            else:
                if self.transform is not None:
                    sample = self.transform(image=sample)['image']
                if self.target_transform is not None:
                    target = self.target_transform(target)

            return sample, target

        def get_cv2_image(self, image):
            if isinstance(image, PIL.Image.Image):
                image_np = np.array(image).astype('uint8')
                return image_np
            elif isinstance(image, np.ndarray):
                return image
            else:
                raise RuntimeError("Only PIL.Image and CV2 loaders currently supported!")

    # Just normalization for validation
    data_transforms = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

    data_dir = 'hymenoptera_data'
    # Just normalization for validation
    data_transforms = A.Compose([
        A.Resize(height=256, width=256),
        A.CenterCrop(height=224, width=224),
        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
        ToTensorV2(),
    ])
    train_dataset = AntsBeesDataset(root=os.path.join(data_dir,'train'))
    train_dataset.transforms = data_transforms

    val_dataset = AntsBeesDataset(root=os.path.join(data_dir,'val'))
    val_dataset.transforms = data_transforms

    dataloaders = {
        'train':torch.utils.data.DataLoader(train_dataset, batch_size=4,
                                                    shuffle=True),
        'val': torch.utils.data.DataLoader(val_dataset, batch_size=4,
                                                    shuffle=True)
    }

    class_names = ['ants', 'bees']

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")








.. GENERATED FROM PYTHON SOURCE LINES 143-146

Visualize a Few Images
~~~~~~~~~~~~~~~~~~~~~~
Let's visualize a few training images so as to understand the data augmentation.

.. GENERATED FROM PYTHON SOURCE LINES 146-168

.. code-block:: default


    def imshow(inp, title=None):
        """Imshow for Tensor."""
        inp = inp.numpy().transpose((1, 2, 0))
        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])
        inp = std * inp + mean
        inp = np.clip(inp, 0, 1)
        plt.imshow(inp)
        if title is not None:
            plt.title(title)
        plt.pause(0.001)  # pause a bit so that plots are updated


    # Get a batch of training data
    inputs, classes = next(iter(dataloaders['train']))

    # Make a grid from batch
    out = torchvision.utils.make_grid(inputs)

    imshow(out, title=[class_names[x] for x in classes])




.. image-sg:: /user-guide/vision/auto_quickstarts/images/sphx_glr_plot_classification_tutorial_001.png
   :alt: ['ants', 'ants', 'bees', 'ants']
   :srcset: /user-guide/vision/auto_quickstarts/images/sphx_glr_plot_classification_tutorial_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 169-176

.. image :: /_static/images/tutorials/ants-bees.png
  :width: 400
  :alt: Ants and Bees

Downloading a pre-trained model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Now, we will download a pre-trained model from torchvision, that was trained on the ImageNet dataset.

.. GENERATED FROM PYTHON SOURCE LINES 176-184

.. code-block:: default


    model = torchvision.models.resnet18(pretrained=True)
    num_ftrs = model.fc.in_features
    # We have only 2 classes
    model.fc = nn.Linear(num_ftrs, 2)
    model = model.to(device)
    _ = model.eval()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /home/runner/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth

      0%|          | 0.00/44.7M [00:00<?, ?B/s]
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     71%|#######1  | 31.8M/44.7M [00:00<00:00, 81.5MB/s]
     89%|########8 | 39.6M/44.7M [00:00<00:00, 79.8MB/s]
    100%|##########| 44.7M/44.7M [00:00<00:00, 81.9MB/s]




.. GENERATED FROM PYTHON SOURCE LINES 185-193

Validating the Model with Deepchecks
=====================================
Now, after we have the training data, validation data and the model, we can validate the model with
deepchecks test suites.

Visualize the data loader and the model outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First we'll make sure we are familiar with the data loader and the model outputs.

.. GENERATED FROM PYTHON SOURCE LINES 193-206

.. code-block:: default


    batch = next(iter(dataloaders['train']))

    print("Batch type is: ", type(batch))
    print("First element is: ", type(batch[0]), "with len of ", len(batch[0]))
    print("Example output of an image shape from the dataloader ", batch[0][0].shape)
    print("Image values", batch[0][0])
    print("-"*80)

    print("Second element is: ", type(batch[1]), "with len of ", len(batch[1]))
    print("Example output of a label shape from the dataloader ", batch[1][0].shape)
    print("Image values", batch[1][0])





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Batch type is:  <class 'list'>
    First element is:  <class 'torch.Tensor'> with len of  4
    Example output of an image shape from the dataloader  torch.Size([3, 224, 224])
    Image values tensor([[[-0.79930, -0.90205, -0.95342,  ...,  2.18041,  2.23178,  2.21466],
             [-0.71367, -0.79930, -0.83355,  ...,  2.04341,  1.54679,  1.64954],
             [-0.54243, -0.62805, -0.69655,  ...,  2.21466,  1.94066,  1.61529],
             ...,
             [-0.81642, -1.10754, -1.38154,  ..., -0.06293, -0.13143, -0.26843],
             [-0.90205, -1.10754, -1.24454,  ..., -0.38830, -0.47393, -0.59380],
             [-0.90205, -1.05617, -1.07329,  ..., -0.74792, -0.71367, -0.78217]],

            [[ 0.18768,  0.04762, -0.09244,  ...,  2.41106,  2.41106,  2.41106],
             [ 0.34524,  0.20518,  0.08263,  ...,  2.34104,  1.76331,  1.72829],
             [ 0.55532,  0.41527,  0.29272,  ...,  2.39356,  2.18347,  1.50070],
             ...,
             [-0.12745, -0.44258, -0.70518,  ...,  0.52031,  0.45028,  0.36275],
             [-0.17997, -0.37255, -0.49510,  ...,  0.22269,  0.10014,  0.01261],
             [-0.10994, -0.26751, -0.28501,  ...,  0.03011, -0.09244, -0.21499]],

            [[-1.14214, -1.19442, -1.21185,  ...,  2.57028,  2.60514,  2.34370],
             [-1.05499, -1.14214, -1.15956,  ...,  2.58771,  2.08227,  1.52453],
             [-0.93298, -1.03756, -1.07242,  ...,  2.46571,  2.34370,  1.97769],
             ...,
             [-1.29900, -1.54301, -1.75216,  ..., -0.60183, -0.60183, -0.56697],
             [-1.36871, -1.54301, -1.68244,  ..., -0.81098, -0.89813, -0.86327],
             [-1.43843, -1.61272, -1.68244,  ..., -1.05499, -1.14214, -1.10728]]])
    --------------------------------------------------------------------------------
    Second element is:  <class 'torch.Tensor'> with len of  4
    Example output of a label shape from the dataloader  torch.Size([])
    Image values tensor(1)




.. GENERATED FROM PYTHON SOURCE LINES 207-213

Implementing the ClassificationData class
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The first step is to implement a class that enables deepchecks to interact with your model and data.
The appropriate class to implement should be selected according to you models task type. In this tutorial,
we will implement the classification task type by implementing a class that inherits from the
:class:`deepchecks.vision.classification_data.ClassificationData` class.

.. GENERATED FROM PYTHON SOURCE LINES 213-252

.. code-block:: default


    # The goal of this class is to make sure the outputs of the model and of the dataloader are in the correct format.
    # To learn more about the expected format please visit the API reference for the
    # :class:`deepchecks.vision.classification_data.ClassificationData` class.

    class AntsBeesData(ClassificationData):

        def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)

        def batch_to_images(self, batch):
            """
            Convert a batch of data to images in the expected format. The expected format is an iterable of cv2 images,
            where each image is a numpy array of shape (height, width, channels). The numbers in the array should be in the
            range [0, 255]
            """
            inp = batch[0].detach().numpy().transpose((0, 2, 3, 1))
            mean = [0.485, 0.456, 0.406]
            std = [0.229, 0.224, 0.225]
            inp = std * inp + mean
            inp = np.clip(inp, 0, 1)
            return inp*255

        def batch_to_labels(self, batch):
            """
            Convert a batch of data to labels in the expected format. The expected format is a tensor of shape (N,),
            where N is the number of samples. Each element is an integer representing the class index.
            """
            return batch[1]

        def infer_on_batch(self, batch, model, device):
            """
            Returns the predictions for a batch of data. The expected format is a tensor of shape (N, n_classes),
            where N is the number of samples. Each element is an array of length n_classes that represent the probability of
            each class.
            """
            logits = model.to(device)(batch[0].to(device))
            return nn.Softmax(dim=1)(logits)








.. GENERATED FROM PYTHON SOURCE LINES 253-254

After defining the task class, we can validate it by running the following code:

.. GENERATED FROM PYTHON SOURCE LINES 254-265

.. code-block:: default


    LABEL_MAP = {
        0: 'ants',
        1: 'bees'
      }
    training_data = AntsBeesData(data_loader=dataloaders["train"], label_map=LABEL_MAP)
    val_data = AntsBeesData(data_loader=dataloaders["val"], label_map=LABEL_MAP)

    training_data.validate_format(model)
    val_data.validate_format(model)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Deepchecks will try to validate the extractors given...
    Structure validation
    --------------------
    Label formatter: Pass!
    Prediction formatter: Pass!
    Image formatter: Pass!

    Content validation
    ------------------
    For validating the content within the structure you have to manually observe the classes, image, label and prediction.
    Examples of classes observed in the batch's labels: [[1], [0], [1], [1]]
    Visual images & label & prediction: should open in a new window
    *******************************************************************************
    This machine does not support GUI
    The formatted image was saved in:
    /home/runner/work/deepchecks/deepchecks/docs/source/user-guide/vision/quickstarts/deepchecks_formatted_image (2).jpg
    Visual example of an image. Label class 1 Prediction class 0
    validate_extractors can be set to skip the image saving or change the save path
    *******************************************************************************
    Deepchecks will try to validate the extractors given...
    Structure validation
    --------------------
    Label formatter: Pass!
    Prediction formatter: Pass!
    Image formatter: Pass!

    Content validation
    ------------------
    For validating the content within the structure you have to manually observe the classes, image, label and prediction.
    Examples of classes observed in the batch's labels: [[1], [1], [1], [1]]
    Visual images & label & prediction: should open in a new window
    *******************************************************************************
    This machine does not support GUI
    The formatted image was saved in:
    /home/runner/work/deepchecks/deepchecks/docs/source/user-guide/vision/quickstarts/deepchecks_formatted_image (3).jpg
    Visual example of an image. Label class 1 Prediction class 0
    validate_extractors can be set to skip the image saving or change the save path
    *******************************************************************************




.. GENERATED FROM PYTHON SOURCE LINES 266-272

And observe the output:

Running Deepchecks' full suite on our data and model!
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Now that we have defined the task class, we can validate the model with the full suite of deepchecks.
This can be done with this simple few lines of code:

.. GENERATED FROM PYTHON SOURCE LINES 272-278

.. code-block:: default


    from deepchecks.vision.suites import full_suite

    suite = full_suite()
    result = suite.run(training_data, val_data, model, device=device)





.. rst-class:: sphx-glr-script-out

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    |#######################################| 39/39 [Time: 00:05]




    Computing Single Dataset Checks - Test Dataset:
    |       | 0/7 [Time: 00:00]




    Computing Single Dataset Checks - Test Dataset:
    |####   | 4/7 [Time: 00:00, Check=Image Segment Performance]




    Computing Single Dataset Checks - Test Dataset:
    |###### | 6/7 [Time: 00:00, Check=Label Property Outliers]  




    Computing Single Dataset Checks - Test Dataset:
    |#######| 7/7 [Time: 00:00, Check=Property Label Correlation]





    Computing Checks:
    |          | 0/10 [Time: 00:00]





    Computing Checks:
    |          | 0/10 [Time: 00:00, Check=Class Performance]





    Computing Checks:
    |          | 0/10 [Time: 00:00, Check=Train Test Prediction Drift]





    Computing Checks:
    |##        | 2/10 [Time: 00:00, Check=Train Test Prediction Drift]





    Computing Checks:
    |##        | 2/10 [Time: 00:00, Check=Simple Model Comparison]    





    Computing Checks:
    |##        | 2/10 [Time: 00:00, Check=Model Error Analysis]   Default parameter min_samples_leaf will change in version 2.6.See https://github.com/scikit-learn-contrib/category_encoders/issues/327
    Default parameter smoothing will change in version 2.6.See https://github.com/scikit-learn-contrib/category_encoders/issues/327






    Computing Checks:
    |####      | 4/10 [Time: 00:00, Check=Model Error Analysis]





    Computing Checks:
    |####      | 4/10 [Time: 00:00, Check=New Labels]          





    Computing Checks:
    |####      | 4/10 [Time: 00:00, Check=Heatmap Comparison]





    Computing Checks:
    |####      | 4/10 [Time: 00:00, Check=Train Test Label Drift]





    Computing Checks:
    |####      | 4/10 [Time: 00:00, Check=Image Property Drift]  





    Computing Checks:
    |########  | 8/10 [Time: 00:00, Check=Image Property Drift]





    Computing Checks:
    |########  | 8/10 [Time: 00:00, Check=Image Dataset Drift] 





    Computing Checks:
    |########  | 8/10 [Time: 00:00, Check=Property Label Correlation Change]





    Computing Checks:
    |##########| 10/10 [Time: 00:01, Check=Property Label Correlation Change]





    Computing Checks:
    |##########| 10/10 [Time: 00:01, Check=Property Label Correlation Change]
                                           

                                                                                                                          


                                                                                                                  



                                                                                                   




                                                                                                                 





                                                                                               



.. GENERATED FROM PYTHON SOURCE LINES 279-282

Observing the results:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The results can be saved as a html file with the following code:

.. GENERATED FROM PYTHON SOURCE LINES 282-285

.. code-block:: default


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.. rst-class:: sphx-glr-script-out

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.. GENERATED FROM PYTHON SOURCE LINES 286-287

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.. GENERATED FROM PYTHON SOURCE LINES 287-289

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white-space: pre-wrap;\n}\n#T_ca4dc_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ca4dc_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ca4dc_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ca4dc_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ca4dc_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_ca4dc_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_ca4dc_row0_col1\" class=\"data row0 col1\" ><a href=\"#SimpleModelComparison_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\">Simple Model Comparison</a></td>\n      <td id=\"T_ca4dc_row0_col2\" class=\"data row0 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_ca4dc_row0_col3\" class=\"data row0 col3\" >Found metrics with gain below threshold: {'F1': {'ants': '-1.06%', 'bees': '6.82%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_ca4dc_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_ca4dc_row1_col1\" class=\"data row1 col1\" ><a href=\"#ImageSegmentPerformance-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\">Image Segment Performance - Test Dataset</a></td>\n      <td id=\"T_ca4dc_row1_col2\" class=\"data row1 col2\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_ca4dc_row1_col3\" class=\"data row1 col3\" >Properties with failed segments: Brightness: {'Range': '[0.57, inf)', 'Metric': 'Precision', 'Ratio': 0.49}, Mean Blue Relative Intensity: {'Range': '(-inf, 0.14)', 'Metric': 'Precision', 'Ratio': 0.49}, Mean Green Relative Intensity: {'Range': '[0.42, inf)', 'Metric': 'Precision', 'Ratio': 0.49}, M...</td>\n    </tr>\n    <tr>\n      <td id=\"T_ca4dc_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_ca4dc_row2_col1\" class=\"data row2 col1\" ><a href=\"#ImageSegmentPerformance-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\">Image Segment Performance - Train Dataset</a></td>\n      <td id=\"T_ca4dc_row2_col2\" class=\"data row2 col2\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_ca4dc_row2_col3\" class=\"data row2 col3\" >Properties with failed segments: Brightness: {'Range': '[0.43, 0.48)', 'Metric': 'Precision', 'Ratio': 0.67}, Mean Green Relative Intensity: {'Range': '[0.34, 0.37)', 'Metric': 'Precision', 'Ratio': 0.77}, Mean Red Relative Intensity: {'Range': '[0.4, 0.45)', 'Metric': 'Precision', 'Ratio': 0.67}, R...</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "faeb81c6938149abab137b21f9d0a2b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd76f46cc49d483280ceeab64198c424": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17f7a175c2d64dfab4d5fc8e26a04ac3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_db2bb12947344ff281698b3c18a29ece", "style": "IPY_MODEL_95532565d6554a06a591b26c56fd9e23", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "db2bb12947344ff281698b3c18a29ece": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95532565d6554a06a591b26c56fd9e23": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be08261c71ab4c15ae495b5bb18e9cfa": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_f7e656decb5147b18105689c94aced34", "IPY_MODEL_813ef5a9db174bb0ad0b5c91a7fe9777", "IPY_MODEL_e3350ff50d36463dafc131c4d98b1180", "IPY_MODEL_684480bbff0d4fd491b3bcece155c1ab"], "layout": "IPY_MODEL_3fa4bff9e8474b02bfbdc3f6da4acc5c"}}, "f7e656decb5147b18105689c94aced34": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bee03406a3944e41be516761b8cb8ef7", "style": "IPY_MODEL_c7d7d53b552f41ef905cf714f73656ef", "value": "<h4 id=\"SimpleModelComparison_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\"><b>Simple Model Comparison</b></h4>"}}, "bee03406a3944e41be516761b8cb8ef7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c7d7d53b552f41ef905cf714f73656ef": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "813ef5a9db174bb0ad0b5c91a7fe9777": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ca49df00fec4f1bace3b3756c217875", "style": "IPY_MODEL_379e00130ef0486cb29d8bafc3ed7e56", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4ca49df00fec4f1bace3b3756c217875": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "379e00130ef0486cb29d8bafc3ed7e56": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e3350ff50d36463dafc131c4d98b1180": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_76f9b9712ef2439b9ab21e54560a3736", "style": "IPY_MODEL_9b6341654d8d468e8e663f07b556f3aa", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_aeffd_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aeffd_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aeffd_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aeffd_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aeffd_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_aeffd_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_aeffd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_aeffd_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_aeffd_row0_col2\" class=\"data row0 col2\" >Found metrics with gain below threshold: {'F1': {'ants': '-1.06%', 'bees': '6.82%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "76f9b9712ef2439b9ab21e54560a3736": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b6341654d8d468e8e663f07b556f3aa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "684480bbff0d4fd491b3bcece155c1ab": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b058ef89526d475ab5377fc0ae2138ee", "IPY_MODEL_f91bacab52524c70a30efeeaf51d7b0e", "IPY_MODEL_d97e57f2fe38491eaf6c593f112a101c"], "layout": "IPY_MODEL_59cede9f220c4f409d6ed8fec5dc6ce9"}}, "b058ef89526d475ab5377fc0ae2138ee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_204a8ea1410e4b57b48c9dfd23b71db2", "style": "IPY_MODEL_06f7a5cf28234dd49c15b7ca84b65b1e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "204a8ea1410e4b57b48c9dfd23b71db2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "06f7a5cf28234dd49c15b7ca84b65b1e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f91bacab52524c70a30efeeaf51d7b0e": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple Model", "marker": {"color": "#2191FB", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["ants", "bees"], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "aea39aa7-2a40-470b-b6a6-ab717362fa73"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Given Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Given Model", "marker": {"color": "#b287a3", "pattern": {"shape": ""}}, "name": "Given Model", "offsetgroup": "Given Model", "orientation": "v", "showlegend": true, "x": ["ants", "bees"], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "152339b9-48eb-44a2-a728-5464a19ca648"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, 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"radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Simple Model (Strategy: most_frequent) vs. Given Model"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "s6AMq/UW5D8AAAAAAAAAAA=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "Zj9ZApv24z9t0UUXXXSxPw=="}]}, "d97e57f2fe38491eaf6c593f112a101c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c8016c15fc2c4846876fecdb9ff76b0b", "style": "IPY_MODEL_25c2b52d3345490f9350a93ae188225d", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\" style=\"font-size: 14px;\">Go to top</a>"}}, "c8016c15fc2c4846876fecdb9ff76b0b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25c2b52d3345490f9350a93ae188225d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59cede9f220c4f409d6ed8fec5dc6ce9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3fa4bff9e8474b02bfbdc3f6da4acc5c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e88b0876c2b44ce5be7aebf162a430c2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_96f8280b8ba9496e866ea2c2fa69ca51", "style": "IPY_MODEL_ef3cdc076f7c46d2a6dddb5b6b313c7e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "96f8280b8ba9496e866ea2c2fa69ca51": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef3cdc076f7c46d2a6dddb5b6b313c7e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3fa1286ac26541099596af92b25d0d61": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_036bb09ceb9f4d7fa45a6c2c127caea5", "IPY_MODEL_583ddf1e61bc4355870b5792d27f1bf6", "IPY_MODEL_b61b8b6ef8be4e23b77785b17a3e6d7d", "IPY_MODEL_286c1e79770f4822a36d298354e5a18f"], "layout": "IPY_MODEL_555e1f872e2a4e2bb5d7f4ea264d5ad0"}}, "036bb09ceb9f4d7fa45a6c2c127caea5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c4aaf93448a450cb87610d9b84ede80", "style": "IPY_MODEL_90be1138647b4baeb5ff0184b2ec4379", "value": "<h4 id=\"ImageSegmentPerformance-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\"><b>Image Segment Performance - Test Dataset</b></h4>"}}, "0c4aaf93448a450cb87610d9b84ede80": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90be1138647b4baeb5ff0184b2ec4379": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "583ddf1e61bc4355870b5792d27f1bf6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2f2e55663feb499d98cb96eafeb3e077", "style": "IPY_MODEL_2d0566ae492b4d9982d3631f629714e7", "value": "<p>Segment the data by various properties of the image, and compare the performance of the segments. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_image_segment_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "2f2e55663feb499d98cb96eafeb3e077": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2d0566ae492b4d9982d3631f629714e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b61b8b6ef8be4e23b77785b17a3e6d7d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_86fcaa7a15e546aa9a4b8b15d1ef9b59", "style": "IPY_MODEL_b7c018c56f24416aafd3bcfabf62edc4", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_be735_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be735_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be735_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be735_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be735_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_be735_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_be735_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_be735_row0_col1\" class=\"data row0 col1\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_be735_row0_col2\" class=\"data row0 col2\" >Properties with failed segments: Brightness: {'Range': '[0.57, inf)', 'Metric': 'Precision', 'Ratio': 0.49}, Mean Blue Relative Intensity: {'Range': '(-inf, 0.14)', 'Metric': 'Precision', 'Ratio': 0.49}, Mean Green Relative Intensity: {'Range': '[0.42, inf)', 'Metric': 'Precision', 'Ratio': 0.49}, Mean Red Relative Intensity: {'Range': '(-inf, 0.34)', 'Metric': 'Precision', 'Ratio': 0.53}, RMS Contrast: {'Range': '[0.19, 0.22)', 'Metric': 'Precision', 'Ratio': 0.54}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "86fcaa7a15e546aa9a4b8b15d1ef9b59": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7c018c56f24416aafd3bcfabf62edc4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "286c1e79770f4822a36d298354e5a18f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b3ff7ef31c824b198c5bd239c8ab9af8", "IPY_MODEL_e007c6a5589846d992ffb3d8a02bf239", "IPY_MODEL_9a3a1ff2ab014dfb9522a919494ed766"], "layout": "IPY_MODEL_a30880985b0f4ce7bf2084c3d27258a5"}}, "b3ff7ef31c824b198c5bd239c8ab9af8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_69346ff022944bb298f1459433883d99", "style": "IPY_MODEL_d5e6929b59174b1ab4a84868d5b431e4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "69346ff022944bb298f1459433883d99": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5e6929b59174b1ab4a84868d5b431e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e007c6a5589846d992ffb3d8a02bf239": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "customdata": [[31], [30], [31], [30], [31]], "hovertemplate": "Metric=Precision<br>Property=RMS Contrast<br>Range=%{x}<br>Value=%{y}<br>Number of samples=%{customdata[0]}<extra></extra>", "legendgroup": "Precision", "marker": {"color": "rgb(102, 197, 204)", "pattern": {"shape": ""}}, "name": "Precision", "offsetgroup": "Precision", "orientation": "v", "showlegend": true, "textposition": "auto", "width": 0.2, "x": ["(-inf, 0.15)", "[0.15, 0.19)", "[0.19, 0.22)", "[0.22, 0.25)", "[0.25, inf)"], "xaxis": "x", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y", "type": "bar", "uid": "01d856c1-cc62-47b8-8155-e25ac97da66e"}, {"alignmentgroup": "True", "customdata": [[31], [30], [31], [30], [31]], "hovertemplate": "Metric=Precision<br>Property=Mean Red Relative Intensity<br>Range=%{x}<br>Value=%{y}<br>Number of samples=%{customdata[0]}<extra></extra>", "legendgroup": "Precision", "marker": {"color": "rgb(102, 197, 204)", "pattern": {"shape": ""}}, "name": "Precision", "offsetgroup": "Precision", "orientation": "v", "showlegend": false, "textposition": "auto", "width": 0.2, "x": ["(-inf, 0.34)", "[0.34, 0.36)", "[0.36, 0.4)", "[0.4, 0.45)", "[0.45, inf)"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y2", "type": "bar", "uid": "51b68e5f-97e9-4149-bf21-146d098e1fa0"}, {"alignmentgroup": "True", "customdata": [[31], [30], [31], [30], [31]], "hovertemplate": "Metric=Precision<br>Property=Mean Green Relative Intensity<br>Range=%{x}<br>Value=%{y}<br>Number of samples=%{customdata[0]}<extra></extra>", "legendgroup": "Precision", "marker": {"color": "rgb(102, 197, 204)", "pattern": {"shape": ""}}, "name": "Precision", "offsetgroup": "Precision", "orientation": "v", "showlegend": false, "textposition": "auto", "width": 0.2, "x": ["(-inf, 0.33)", "[0.33, 0.34)", "[0.34, 0.36)", "[0.36, 0.42)", "[0.42, inf)"], "xaxis": "x3", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y3", "type": "bar", "uid": "454a400a-bf13-4284-8483-f53421f89e55"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "RMS Contrast", "x": 0.14666666666666667, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Mean Red Relative Intensity", "x": 0.49, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Mean Green Relative Intensity", "x": 0.8333333333333333, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Precision", "textangle": 90, "x": 0.98, "xanchor": "left", "xref": "paper", "y": 0.5, "yanchor": "middle", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Metric"}, "tracegroupgap": 0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], 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{"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Metric Score Per Property Value Segment (showing top 3 properties by diffrence in segments)"}, "xaxis": {"anchor": "y", "domain": [0.0, 0.29333333333333333], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}, "xaxis2": {"anchor": "y2", "domain": [0.3433333333333333, 0.6366666666666667], "type": "category"}, "yaxis2": {"anchor": "x2", "domain": [0.0, 1.0], "matches": "y", "showticklabels": true}, "xaxis3": {"anchor": "y3", "domain": [0.6866666666666666, 0.98], "type": "category"}, "yaxis3": {"anchor": "x3", "domain": [0.0, 1.0], "matches": "y", "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "VVVVVVVV1T/uaYTlnkboP4QQQgghhNA/kiRJkiRJ2j9ERERERETkPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "11prrbXWyj8AAAAAAADQP4ZhGIZhGOI/R1juaYTlzj/v7u7u7u7mPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "sNzTCMs93T9mZmZmZmbWP+/u7u7u7uY/5p5GWO5p5D/e3d3d3d3NPw=="}]}, "9a3a1ff2ab014dfb9522a919494ed766": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1d103d61ccdd450b819ab9cf7fc53afb", "style": "IPY_MODEL_ebc3b35ce32a4fecae500649c8611e18", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\" style=\"font-size: 14px;\">Go to top</a>"}}, "1d103d61ccdd450b819ab9cf7fc53afb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ebc3b35ce32a4fecae500649c8611e18": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a30880985b0f4ce7bf2084c3d27258a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "555e1f872e2a4e2bb5d7f4ea264d5ad0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25808e921f49458c951111d4060fbfc8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_82d35cd926a140089967aab23f3b9968", "IPY_MODEL_49c7282058c74a9eb42b465db93e52f9", "IPY_MODEL_97517bc2c4fb4c19a3fb5b096810f356", "IPY_MODEL_8d117f308cef44a6ae511d55de915985"], "layout": "IPY_MODEL_08bc13e70f0f4ee1b7ce3b9338c0417b"}}, "82d35cd926a140089967aab23f3b9968": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_187f812792b044e58370403f9f301b9a", "style": "IPY_MODEL_0d078064627743e1a62b0d05b48bdeb7", "value": "<h4 id=\"ImageSegmentPerformance-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\"><b>Image Segment Performance - Train Dataset</b></h4>"}}, "187f812792b044e58370403f9f301b9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0d078064627743e1a62b0d05b48bdeb7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49c7282058c74a9eb42b465db93e52f9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_20bef485f89841d3ab652d1378ca6f4a", "style": "IPY_MODEL_5ebb0b445d5548c5a920e194029ef7be", "value": "<p>Segment the data by various properties of the image, and compare the performance of the segments. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_image_segment_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "20bef485f89841d3ab652d1378ca6f4a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ebb0b445d5548c5a920e194029ef7be": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "97517bc2c4fb4c19a3fb5b096810f356": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_102feb91ee3149c4b0718fd289446503", "style": "IPY_MODEL_486e772eef5549d69cacde4db97e83ff", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_2e060_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e060_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e060_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e060_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e060_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2e060_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_2e060_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2e060_row0_col1\" class=\"data row0 col1\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_2e060_row0_col2\" class=\"data row0 col2\" >Properties with failed segments: Brightness: {'Range': '[0.43, 0.48)', 'Metric': 'Precision', 'Ratio': 0.67}, Mean Green Relative Intensity: {'Range': '[0.34, 0.37)', 'Metric': 'Precision', 'Ratio': 0.77}, Mean Red Relative Intensity: {'Range': '[0.4, 0.45)', 'Metric': 'Precision', 'Ratio': 0.67}, RMS Contrast: {'Range': '[0.23, 0.25)', 'Metric': 'Precision', 'Ratio': 0.55}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "102feb91ee3149c4b0718fd289446503": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "486e772eef5549d69cacde4db97e83ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8d117f308cef44a6ae511d55de915985": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_88845ea5977b40a2a2c786b0752ac8f7", "IPY_MODEL_d812db0a25544351ae87ce8fa0c0c9d1", "IPY_MODEL_55583b4f80734c11910bb2688f7fce55"], "layout": "IPY_MODEL_5264eb330e0447afa60b9730bf207089"}}, "88845ea5977b40a2a2c786b0752ac8f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2c3d45ef1aad4e439025f58dec7e4d96", "style": "IPY_MODEL_736f403ee148454a94ad40f378253441", "value": "<h5><b>Additional Outputs</b></h5>"}}, "2c3d45ef1aad4e439025f58dec7e4d96": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "736f403ee148454a94ad40f378253441": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d812db0a25544351ae87ce8fa0c0c9d1": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "customdata": [[49], [49], [48], [49], [49]], "hovertemplate": 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href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-T77GG\" style=\"font-size: 14px;\">Go to top</a>"}}, "9af1d97e4f1f4b94b30cf8fa4f4848db": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f008b8a235ed47558cebdb8d382d1d3a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5264eb330e0447afa60b9730bf207089": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "08bc13e70f0f4ee1b7ce3b9338c0417b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "087594db892a4733a51f5ad0a1748613": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b25450ecab58484c89fd47fafafa1f67": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "593d6435e37f4411a5afa4074446b14a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ee94f3bac2a4ee8b2d4bb24d03b31eb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c940650326754021a03f17acb3bf2c64", "IPY_MODEL_8103780bceb746cdbef693deb6e489bb"], "layout": "IPY_MODEL_01d98ff090e8452c8950fe4779dd982f"}}, "c940650326754021a03f17acb3bf2c64": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95577f1adfd14672af99cfcda769c315", "style": "IPY_MODEL_b2a7337cf64b4ede83a095e384b908cd", "value": "<span id=\"summary_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\"></span>"}}, "95577f1adfd14672af99cfcda769c315": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b2a7337cf64b4ede83a095e384b908cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8103780bceb746cdbef693deb6e489bb": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_d302c80985df4cf1bdb3e786f4b6096c"], "layout": "IPY_MODEL_8f34d5492a0e4554b7355c0d963cc26c", "selected_index": null}}, "d302c80985df4cf1bdb3e786f4b6096c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c23f58d8c1014a23a1b78f6adf1a296f", "IPY_MODEL_80f2430cadd246a5ae543980ffd9b77b", "IPY_MODEL_655916e2e9f64b4fbd1371a644672716", "IPY_MODEL_9743eae1746c4835be31056584b7cbd2", "IPY_MODEL_770042d7738c4a1f993e2ab2ebc4722a", "IPY_MODEL_9743eae1746c4835be31056584b7cbd2", "IPY_MODEL_8899eb1a483c4f7188aaa5eab1d774c3", "IPY_MODEL_9743eae1746c4835be31056584b7cbd2", "IPY_MODEL_37197473405d4f69b77373ea1686a986", "IPY_MODEL_9743eae1746c4835be31056584b7cbd2", "IPY_MODEL_0a3ada2cc88b43a2a12130bee52a05fa"], "layout": "IPY_MODEL_f195894373bf4d11832047951f5719b4"}}, "c23f58d8c1014a23a1b78f6adf1a296f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_787677b69dd14d0d848b1ab4fbc298f4", "style": "IPY_MODEL_6374be7d207d4dd08e7684d0992a0302", "value": "<style type=\"text/css\">\n#T_8b600_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b600_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b600_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b600_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b600_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8b600_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_8b600_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8b600_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\">Class Performance</a></td>\n      <td id=\"T_8b600_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_8b600_row0_col3\" class=\"data row0 col3\" >Found max degradation of 7.72% for metric Precision and class ants.</td>\n    </tr>\n    <tr>\n      <td id=\"T_8b600_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8b600_row1_col1\" class=\"data row1 col1\" ><a href=\"#TrainTestLabelDrift_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\">Train Test Label Drift</a></td>\n      <td id=\"T_8b600_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_8b600_row1_col3\" class=\"data row1 col3\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_8b600_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8b600_row2_col1\" class=\"data row2 col1\" ><a href=\"#ImagePropertyDrift_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\">Image Property Drift</a></td>\n      <td id=\"T_8b600_row2_col2\" class=\"data row2 col2\" >Earth Mover's Distance < 0.1 for image properties drift</td>\n      <td id=\"T_8b600_row2_col3\" class=\"data row2 col3\" >Found property RMS Contrast with largest Earth Mover's Distance score 0.04</td>\n    </tr>\n    <tr>\n      <td id=\"T_8b600_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8b600_row3_col1\" class=\"data row3 col1\" ><a href=\"#FeatureLabelCorrelationChange_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\">Feature Label Correlation Change</a></td>\n      <td id=\"T_8b600_row3_col2\" class=\"data row3 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_8b600_row3_col3\" class=\"data row3 col3\" >Found highest PPS 0.1 for property Brightness and class ants</td>\n    </tr>\n    <tr>\n      <td id=\"T_8b600_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8b600_row4_col1\" class=\"data row4 col1\" ><a href=\"#TrainTestPredictionDrift_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\">Train Test Prediction Drift</a></td>\n      <td id=\"T_8b600_row4_col2\" class=\"data row4 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075 for prediction drift</td>\n      <td id=\"T_8b600_row4_col3\" class=\"data row4 col3\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_8b600_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8b600_row5_col1\" class=\"data row5 col1\" >New Labels</td>\n      <td id=\"T_8b600_row5_col2\" class=\"data row5 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_8b600_row5_col3\" class=\"data row5 col3\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "787677b69dd14d0d848b1ab4fbc298f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6374be7d207d4dd08e7684d0992a0302": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "80f2430cadd246a5ae543980ffd9b77b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2e71b459680147c7acc78b76e7faa43e", "style": "IPY_MODEL_d8aaaa7547d54bd0bc032d2f7f511333", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "2e71b459680147c7acc78b76e7faa43e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8aaaa7547d54bd0bc032d2f7f511333": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "655916e2e9f64b4fbd1371a644672716": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_cbf2feca48e745f289871e40749016b6", "IPY_MODEL_991f5692986d4d5db44bd7c9f95b2170", "IPY_MODEL_3e9edd43476c4f4187bfbd474cb37e49", "IPY_MODEL_6d909ed79ad14fb88959ef28963d697c"], "layout": "IPY_MODEL_3e0fc420a95e4008bfa4b32bdfc0480a"}}, "cbf2feca48e745f289871e40749016b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8b9c50c883b342e88a297f2bd52766f7", "style": "IPY_MODEL_8904fa94cea64c26b482fc6e78678f00", "value": "<h4 id=\"ClassPerformance_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\"><b>Class Performance</b></h4>"}}, "8b9c50c883b342e88a297f2bd52766f7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8904fa94cea64c26b482fc6e78678f00": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "991f5692986d4d5db44bd7c9f95b2170": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ae476899545844109347bfd0b22bb05d", "style": "IPY_MODEL_582e6f09b8194d21bd0bee9d5b45f219", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ae476899545844109347bfd0b22bb05d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "582e6f09b8194d21bd0bee9d5b45f219": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3e9edd43476c4f4187bfbd474cb37e49": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e201c3a8fd76458a9e7507a5dc158516", "style": "IPY_MODEL_09d6a978997443b885fae5c5951d4ae8", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_bb148_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bb148_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bb148_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bb148_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bb148_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_bb148_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_bb148_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bb148_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_bb148_row0_col2\" class=\"data row0 col2\" >Found max degradation of 7.72% for metric Precision and class ants.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e201c3a8fd76458a9e7507a5dc158516": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "09d6a978997443b885fae5c5951d4ae8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6d909ed79ad14fb88959ef28963d697c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_35fc681a938b481fb8ada6067e568665", "IPY_MODEL_49168984e948481485ef906ba0ceff83", "IPY_MODEL_4782212292c04607bbe09ca3b9b955c0"], "layout": "IPY_MODEL_e498dd56a5d44a9da6bdc1361b59daba"}}, "35fc681a938b481fb8ada6067e568665": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_36377971f56b418e8041830abee74add", "style": "IPY_MODEL_3934f4ac99dd47ecb6fff32b60618ffc", "value": "<h5><b>Additional Outputs</b></h5>"}}, "36377971f56b418e8041830abee74add": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3934f4ac99dd47ecb6fff32b60618ffc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49168984e948481485ef906ba0ceff83": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["ants", "bees"], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "39de0e08-1664-4a48-8dce-b60d0274d7d5"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["ants", "bees"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "histogram", "uid": "6435facc-6336-4605-a13c-ed2adbf26d83"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": ["ants", "bees"], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "ffc95457-c44a-41c4-8bd1-94c8c5dd19ff"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": 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"buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "9n5Z75f17j+QnOZr9eyAPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "oEUxTrnd3z+amZmZmZnJPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "X/EVX/EV7z+/9pDLioGiPw=="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "MzMzMzMz4z991g2myGfdPw=="}]}, "4782212292c04607bbe09ca3b9b955c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7cf9d129aa5c4f67a978b6c8db66191c", "style": "IPY_MODEL_c5667f32d8a242b6ac16aad20894f8b5", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\" style=\"font-size: 14px;\">Go to top</a>"}}, "7cf9d129aa5c4f67a978b6c8db66191c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c5667f32d8a242b6ac16aad20894f8b5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e498dd56a5d44a9da6bdc1361b59daba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e0fc420a95e4008bfa4b32bdfc0480a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9743eae1746c4835be31056584b7cbd2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_03eaa0c0797b478499233c1112ea0f26", "style": "IPY_MODEL_9aa65bb7a13644e6923ec60102bf7de1", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "03eaa0c0797b478499233c1112ea0f26": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9aa65bb7a13644e6923ec60102bf7de1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "770042d7738c4a1f993e2ab2ebc4722a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_460e16a7a4f940ad8074c79b3549efb8", "IPY_MODEL_1ffc6b5eb008459ba9efa999b8dd269a", "IPY_MODEL_8c350f20ad57408598539156033ca58e", "IPY_MODEL_6c653997f7dd469c9420f80f4c6ead3b"], "layout": "IPY_MODEL_d509a25c70714b0bb90ac844de3acc50"}}, "460e16a7a4f940ad8074c79b3549efb8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fcb670ddd6824b91b2452982ddbb7772", "style": "IPY_MODEL_b5fafd1885ca49c3aeb60bbcf103e962", "value": "<h4 id=\"TrainTestLabelDrift_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\"><b>Train Test Label Drift</b></h4>"}}, "fcb670ddd6824b91b2452982ddbb7772": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5fafd1885ca49c3aeb60bbcf103e962": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1ffc6b5eb008459ba9efa999b8dd269a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_80e47c9bcb0d4da4b25dc686152500ea", "style": "IPY_MODEL_14b3b158bfcd43818ef498ac97b44298", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/train_test_validation/plot_train_test_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "80e47c9bcb0d4da4b25dc686152500ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "14b3b158bfcd43818ef498ac97b44298": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8c350f20ad57408598539156033ca58e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_67017a5a102843a4a5b75c6ea459b22b", "style": "IPY_MODEL_6ccc6ddfde694d18958e006ce780605e", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_4fb7c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4fb7c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4fb7c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4fb7c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4fb7c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_4fb7c_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_4fb7c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_4fb7c_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_4fb7c_row0_col2\" class=\"data row0 col2\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "67017a5a102843a4a5b75c6ea459b22b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ccc6ddfde694d18958e006ce780605e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6c653997f7dd469c9420f80f4c6ead3b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_592c20dffd5f4f0abadc7ec8063f19c4", "IPY_MODEL_c10478a3817f4b91878be6ed24e13cd9", "IPY_MODEL_da34de168bee44229d1b883b80119e47", "IPY_MODEL_44ef51366794444da92d9879ae181de9", "IPY_MODEL_319f07215a15499c9fdcc3fdc8a27df6"], "layout": "IPY_MODEL_354ff5ddf22e42f98985c18bc8a31867"}}, "592c20dffd5f4f0abadc7ec8063f19c4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_60e817ce29ff4400955c20b185a70839", "style": "IPY_MODEL_dd4d9cafacef44a08f899ee5f887cc36", "value": "<h5><b>Additional Outputs</b></h5>"}}, "60e817ce29ff4400955c20b185a70839": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd4d9cafacef44a08f899ee5f887cc36": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c10478a3817f4b91878be6ed24e13cd9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2dc35d3210194759a6a5be91e2f1ef96", "style": "IPY_MODEL_e9531dc04cc74dca95834c7ce99f1d57", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following label properties: ['Samples Per Class'].</span></div>"}}, "2dc35d3210194759a6a5be91e2f1ef96": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e9531dc04cc74dca95834c7ce99f1d57": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da34de168bee44229d1b883b80119e47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_251022966a2340689d29bb9ba737db5d", "style": "IPY_MODEL_9efd9d624b7b4aff9e7e7a026e734839", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "251022966a2340689d29bb9ba737db5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": 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{"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "tHmf4pIh4D+XDME62rzfPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "nfJHnfJH3T+xBlyxBlzhPw=="}]}, "319f07215a15499c9fdcc3fdc8a27df6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_886e752b4ab44483850c0c945c4eecff", "style": "IPY_MODEL_c7dd251e2fe84f24be3219b7d678613c", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\" style=\"font-size: 14px;\">Go to top</a>"}}, "886e752b4ab44483850c0c945c4eecff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c7dd251e2fe84f24be3219b7d678613c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "354ff5ddf22e42f98985c18bc8a31867": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d509a25c70714b0bb90ac844de3acc50": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8899eb1a483c4f7188aaa5eab1d774c3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_bf03f25085844da797353325f8b61d30", "IPY_MODEL_71d854e57a6a417caf5ebf4dbfdcaed5", "IPY_MODEL_8d4246ec18744c89958d89172190cf99", "IPY_MODEL_40aca683ee25419ebdc88bc77e7bf962"], "layout": "IPY_MODEL_9ccd310f2a27459e8baf2e6a497ab7c0"}}, "bf03f25085844da797353325f8b61d30": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c7665c3fb3cc42f1a955123ad32f0163", "style": "IPY_MODEL_685f4effc1ce4d6fbb1b5dd6f57ad3b1", "value": "<h4 id=\"ImagePropertyDrift_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\"><b>Image Property Drift</b></h4>"}}, "c7665c3fb3cc42f1a955123ad32f0163": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "685f4effc1ce4d6fbb1b5dd6f57ad3b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "71d854e57a6a417caf5ebf4dbfdcaed5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_52fe10eb794f4c1e88d8e5a74c962bf2", "style": "IPY_MODEL_1b610124179c47209f67255f95d2d3f4", "value": "<p>    Calculate drift between train dataset and test dataset per image property, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/train_test_validation/plot_image_property_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "52fe10eb794f4c1e88d8e5a74c962bf2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b610124179c47209f67255f95d2d3f4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8d4246ec18744c89958d89172190cf99": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_df9b7296902b4d969092a45d284cdddf", "style": "IPY_MODEL_1109bcbaf30047fdb16696276c0de712", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6d5a6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6d5a6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6d5a6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6d5a6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6d5a6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6d5a6_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6d5a6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6d5a6_row0_col1\" class=\"data row0 col1\" >Earth Mover's Distance < 0.1 for image properties drift</td>\n      <td id=\"T_6d5a6_row0_col2\" class=\"data row0 col2\" >Found property RMS Contrast with largest Earth Mover's Distance score 0.04</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "df9b7296902b4d969092a45d284cdddf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1109bcbaf30047fdb16696276c0de712": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "40aca683ee25419ebdc88bc77e7bf962": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_39c9237b9e934dc19af490fba2d2c7fb", "IPY_MODEL_910cad286f394cd1bcbe0e3a1a3cf484", "IPY_MODEL_fc474973cc6d46339872a1a10a96aa05", "IPY_MODEL_a2a5686a288249cbb098dd2601c1ff62", "IPY_MODEL_9ac763b8b8f74c3eb26afca7335ba53b", "IPY_MODEL_1d7462f97d5f4ced944effa2a8cb0aa0", "IPY_MODEL_1e29352530de429cabcc94942fa5e666", "IPY_MODEL_2daa4b06f6f4447c9523128517b1ca71", "IPY_MODEL_c4689741e76a41eaadeedb49c0073a61", "IPY_MODEL_1af7afdd958c4960b967c1cdca1ed150", "IPY_MODEL_3ffb52f451cd457a9c5f419810988df6"], "layout": "IPY_MODEL_1d41533984db4693a81e7bf80d866bb2"}}, "39c9237b9e934dc19af490fba2d2c7fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_293f0584aa994d269f1a08d65c95f96c", "style": "IPY_MODEL_5d37ce4e68cf40ef8cc8f7f5077b6384", "value": "<h5><b>Additional Outputs</b></h5>"}}, "293f0584aa994d269f1a08d65c95f96c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d37ce4e68cf40ef8cc8f7f5077b6384": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "910cad286f394cd1bcbe0e3a1a3cf484": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_49d3d9b00e5c483eb64bc0250529d268", "style": "IPY_MODEL_d3ea6318468143458e7c579e51bbb3b8", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].</span></div>"}}, "49d3d9b00e5c483eb64bc0250529d268": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3ea6318468143458e7c579e51bbb3b8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fc474973cc6d46339872a1a10a96aa05": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a24ff036822446a69bca1fbe3807e87d", "style": "IPY_MODEL_3429972efeb7439c9fd94824470b8d84", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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{"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c36f58fde88d4bb0b39bfa6f8a406e1d", "style": "IPY_MODEL_82a7c7e858714cf7a5038ba1be1b6df1", "value": "<h4 id=\"FeatureLabelCorrelationChange_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\"><b>Feature Label Correlation Change</b></h4>"}}, "c36f58fde88d4bb0b39bfa6f8a406e1d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "82a7c7e858714cf7a5038ba1be1b6df1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0c692d4b7c1e444985bcbc705c0dd1f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cb5e7af40672437e895f6aaa7f6bce9f", "style": "IPY_MODEL_207900a5689749388909b1a1969e9f9b", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/train_test_validation/plot_property_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "cb5e7af40672437e895f6aaa7f6bce9f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "207900a5689749388909b1a1969e9f9b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9172dc3a1d37492dbca7cfb2bfbec136": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d91c0d20ce5144248323187ab0459aee", "style": "IPY_MODEL_b4befe6bf73542bfa6219f7e3f74bd53", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_63f23_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63f23_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63f23_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63f23_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63f23_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_63f23_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_63f23_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_63f23_row0_col1\" class=\"data row0 col1\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_63f23_row0_col2\" class=\"data row0 col2\" >Found highest PPS 0.1 for property Brightness and class ants</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d91c0d20ce5144248323187ab0459aee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4befe6bf73542bfa6219f7e3f74bd53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3d57425847394678a253acef9f7c3f2a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ef8497928703460f93ddbe820007faf8", "IPY_MODEL_c9dd84f6f72f4b4ebdf602ee4ee04b0a", "IPY_MODEL_82e8b65ddb0842ed932d35b7e67e076d", "IPY_MODEL_aa3bf526b2cd44598888d63deba4cd19", "IPY_MODEL_10d4f2a38e0c4c49a1091275878854be", "IPY_MODEL_0ec4cab72cb44b75a6afdf4955f26b00", "IPY_MODEL_b70a0e796b81485fa73ff5f930b7841e", "IPY_MODEL_f618964222174f70b135da88404bb424", 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(Read more about <a href=https://docs.deepchecks.com/en/stable/checks_gallery/vision/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "6332cdcacdc545e8a163c1a4e9f11b6c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f9777a1840344d4a7e1255e93cd6a29": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "10d4f2a38e0c4c49a1091275878854be": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95c1ba11f0644af0889c98ce81d3b921", "style": "IPY_MODEL_ad2cae6826b3483c8708b3ef516b9ad0", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "95c1ba11f0644af0889c98ce81d3b921": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad2cae6826b3483c8708b3ef516b9ad0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ec4cab72cb44b75a6afdf4955f26b00": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47ede7f335954d80906a8b96da0eade5", "style": "IPY_MODEL_33985ddd44a343e9999f03a031671750", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "47ede7f335954d80906a8b96da0eade5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33985ddd44a343e9999f03a031671750": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b70a0e796b81485fa73ff5f930b7841e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c2bb2a05bf344459f03950897c3cbb5", "style": "IPY_MODEL_616bbd94a97e41b88458228fb0ad204a", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict   the label successfully, using simple classic ML algorithms</div>"}}, "9c2bb2a05bf344459f03950897c3cbb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "616bbd94a97e41b88458228fb0ad204a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f618964222174f70b135da88404bb424": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_49cf045096ad43c486fbb70f5f6948ad", "style": "IPY_MODEL_6a153ba0f6fe42ed85446091b9021195", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "49cf045096ad43c486fbb70f5f6948ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6a153ba0f6fe42ed85446091b9021195": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "735f033909ec4a10bb09e71c10fb7099": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_17c4c55158564b0ba793e0158698a367", "style": "IPY_MODEL_250266d69a5f4382b3beb3417307b90b", "value": "<div>   An even more powerful indication of dataset bias, as an image property that was powerful in train</div>"}}, "17c4c55158564b0ba793e0158698a367": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "250266d69a5f4382b3beb3417307b90b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "db786f5597124612a1d85acdf15371b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3c446f6a6f7248d189ca84a7112fc65d", "style": "IPY_MODEL_ecb2a7a2a5624b6baa6d487a2869a425", "value": "<div>   but not in test can be explained by bias in train that is not relevant to a new dataset.</div>"}}, "3c446f6a6f7248d189ca84a7112fc65d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ecb2a7a2a5624b6baa6d487a2869a425": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c25cd9d1ca04cc0b01ad268506b3a47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cf471f9c3be44d9a87fc366cc94c5dde", "style": "IPY_MODEL_0f5d7a3db0844a9b8cae6f4fcd3f5cc4", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "cf471f9c3be44d9a87fc366cc94c5dde": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0f5d7a3db0844a9b8cae6f4fcd3f5cc4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6bfbac033a0c4e38b9697a672244bcd6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2611b2998a2048ce8304ce4822f78889", "style": "IPY_MODEL_5f5a9cac2a1d4f379237c1e7340f6a2e", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "2611b2998a2048ce8304ce4822f78889": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5f5a9cac2a1d4f379237c1e7340f6a2e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9118e74360cf4e6f9986ed0754a2df29": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c37c7f6c5fd4a6886bacd80490e3e72", "style": "IPY_MODEL_9a7bda1c6fd04b96a770080a5dd118f3", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\" style=\"font-size: 14px;\">Go to top</a>"}}, "9c37c7f6c5fd4a6886bacd80490e3e72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a7bda1c6fd04b96a770080a5dd118f3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "33ba75a44fd54af9a004f52d96883fed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7c596cd5bd47424db2b7ab860a798889": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a3ada2cc88b43a2a12130bee52a05fa": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_57d2bad585184e0cbe2dead69559ffd5", "IPY_MODEL_836a27e03f634611ba56b11b05d7d81a", "IPY_MODEL_392a5f0940d941c2ace5185b9d27b361", "IPY_MODEL_ff36d64ed15143c3b100884e333c1e09"], "layout": "IPY_MODEL_3540f625c0914e8aa113f4bfd87b570d"}}, "57d2bad585184e0cbe2dead69559ffd5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f92f8ec7ef204e0eba3d3d3edc922488", "style": "IPY_MODEL_30f4bb477c884c1dbfeb1487ddf73153", "value": "<h4 id=\"TrainTestPredictionDrift_YR8LNOEMQGY27VYLIENFNVUPR-section-KG4W5\"><b>Train Test Prediction Drift</b></h4>"}}, "f92f8ec7ef204e0eba3d3d3edc922488": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "30f4bb477c884c1dbfeb1487ddf73153": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "836a27e03f634611ba56b11b05d7d81a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5590e0a9027b45beacc8c90610b725a1", "style": "IPY_MODEL_bc1438cc966d4f279df2b7805da930b1", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_train_test_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5590e0a9027b45beacc8c90610b725a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc1438cc966d4f279df2b7805da930b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "392a5f0940d941c2ace5185b9d27b361": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f866022e28fa424896b7b23eab56fe57", "style": "IPY_MODEL_9513b97de8f24a8fa3b6d1ca531bf4d8", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_ebab2_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ebab2_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ebab2_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ebab2_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ebab2_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ebab2_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_ebab2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_ebab2_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075 for prediction drift</td>\n      <td id=\"T_ebab2_row0_col2\" class=\"data row0 col2\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f866022e28fa424896b7b23eab56fe57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9513b97de8f24a8fa3b6d1ca531bf4d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ff36d64ed15143c3b100884e333c1e09": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4836c98de5c54273b57382016e4614f0", "IPY_MODEL_50c74429c06c471abbfc8a52c0394c7c", "IPY_MODEL_d2c33cf900c94659b1baa084a19974f5", "IPY_MODEL_1dd0aeee6111480da4af4b365e426dfb", "IPY_MODEL_d5075b0442434d5da0cafce6de0f496e"], "layout": "IPY_MODEL_ae832403d98c4b87a7f682ee6899e31a"}}, "4836c98de5c54273b57382016e4614f0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a77d601dcc4a4e5f89f508c80508d5e8", "style": "IPY_MODEL_7868c54937b4488abc8630410b8d879a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a77d601dcc4a4e5f89f508c80508d5e8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7868c54937b4488abc8630410b8d879a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "50c74429c06c471abbfc8a52c0394c7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1ee082d49a43427e84d879bf2c98fbb1", "style": "IPY_MODEL_13260ebe65154fd9980aa43e7839777a", "value": "<div><span>The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following prediction properties: ['Samples Per Class']. </span></div>"}}, "1ee082d49a43427e84d879bf2c98fbb1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13260ebe65154fd9980aa43e7839777a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d2c33cf900c94659b1baa084a19974f5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f6e50bf62064d4e936e58d338bdd721", "style": "IPY_MODEL_9919e89500a34fb281d7e617fc0b01a4", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "4f6e50bf62064d4e936e58d338bdd721": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9919e89500a34fb281d7e617fc0b01a4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1dd0aeee6111480da4af4b365e426dfb": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "1b8033bd-13cc-4eea-b24c-ba4f902f2507"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["ants", "bees"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "bar", "uid": "81c7a33b-a453-4b32-acca-6873526087fa"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": ["ants", "bees"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "bar", "uid": "e674a120-8424-4e7d-a964-fdc6f4c1b664"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"font": {"size": 16}, "showarrow": false, "text": "Drift Score (Cramer's V)", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0000000000000002, "yanchor": "bottom", "yref": "paper"}, {"font": {"size": 16}, "showarrow": false, "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper"}], "bargroupgap": 0, "height": 400, "legend": {"title": {"text": "Legend"}, "y": 0.6, "yanchor": "top"}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": 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"IPY_MODEL_7f312f6a72504cb68720870e8d185a18", "IPY_MODEL_b309e1bc8ef54842af42881d800bb7ec", "IPY_MODEL_7f312f6a72504cb68720870e8d185a18", "IPY_MODEL_320a3904f4ce4a28b46ab07bfa8ffb60", "IPY_MODEL_7f312f6a72504cb68720870e8d185a18", "IPY_MODEL_78db1ec609f4493499c938dcda1b2910", "IPY_MODEL_7f312f6a72504cb68720870e8d185a18", "IPY_MODEL_1614add356fb43e6af36146be082dd69"], "layout": "IPY_MODEL_d5530abb214f4d518b6594dd06e1e95d"}}, "a852c8305c7648bf814bb65b59a2d5fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0d87e866596a47f296add0550f28eee0", "style": "IPY_MODEL_83475718c32c4948bb41b1065a7b1e92", "value": "<style type=\"text/css\">\n#T_8248c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8248c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8248c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8248c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8248c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8248c_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_8248c_row0_col0\" class=\"data row0 col0\" >Image Dataset Drift</td>\n      <td id=\"T_8248c_row0_col1\" class=\"data row0 col1\" >Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_8248c_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\">Image Property Outliers - Test Dataset</a></td>\n      <td id=\"T_8248c_row1_col1\" class=\"data row1 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_8248c_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\">Image Property Outliers - Train Dataset</a></td>\n      <td id=\"T_8248c_row2_col1\" class=\"data row2 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_8248c_row3_col0\" class=\"data row3 col0\" ><a href=\"#PropertyLabelCorrelation-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\">Property Label Correlation - Test Dataset</a></td>\n      <td id=\"T_8248c_row3_col1\" class=\"data row3 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_8248c_row4_col0\" class=\"data row4 col0\" ><a href=\"#PropertyLabelCorrelation-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\">Property Label Correlation - Train Dataset</a></td>\n      <td id=\"T_8248c_row4_col1\" class=\"data row4 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_8248c_row5_col0\" class=\"data row5 col0\" ><a href=\"#ConfusionMatrix-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\">Confusion Matrix - Test Dataset</a></td>\n      <td id=\"T_8248c_row5_col1\" class=\"data row5 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_8248c_row6_col0\" class=\"data row6 col0\" ><a href=\"#ConfusionMatrix-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\">Confusion Matrix - Train Dataset</a></td>\n      <td id=\"T_8248c_row6_col1\" class=\"data row6 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_8248c_row7_col0\" class=\"data row7 col0\" ><a href=\"#HeatmapComparison_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\">Heatmap Comparison</a></td>\n      <td id=\"T_8248c_row7_col1\" class=\"data row7 col1\" >Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0d87e866596a47f296add0550f28eee0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "83475718c32c4948bb41b1065a7b1e92": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ae521557131f42909280800396ac64e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_408aa1152d7b47c7b76959d7d6944dfb", "style": "IPY_MODEL_149c5136fceb4cd1bbbd49d03a78895e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "408aa1152d7b47c7b76959d7d6944dfb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "149c5136fceb4cd1bbbd49d03a78895e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1f3d3f3ffbf04dfd8292721869bbda2b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_a1393c2edb9b484bbad6d100aaa20a82", "IPY_MODEL_b04ae62b26db4cdcb12fc8d29a1c983e", "IPY_MODEL_d63258f254114df5aa0db667d75c454f"], "layout": "IPY_MODEL_96c59b901c3d4245be70e169e0594c5e"}}, "a1393c2edb9b484bbad6d100aaa20a82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f774955f12f645e39010f2c848717b3c", "style": "IPY_MODEL_fc6d2d8450bb45d8abe599c87c64cd8d", "value": "<h4 id=\"ImagePropertyOutliers-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\"><b>Image Property Outliers - Test Dataset</b></h4>"}}, "f774955f12f645e39010f2c848717b3c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc6d2d8450bb45d8abe599c87c64cd8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b04ae62b26db4cdcb12fc8d29a1c983e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3457ec539d5b41e196799fabcb9f009c", "style": "IPY_MODEL_dfa3fc47c988411c97c9f46f50f56cb7", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3457ec539d5b41e196799fabcb9f009c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dfa3fc47c988411c97c9f46f50f56cb7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d63258f254114df5aa0db667d75c454f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2545cbb1295b4cde9d459eb4a3f19691", "IPY_MODEL_3aed222ad6b04da390e498eea952f956", "IPY_MODEL_a5a926f7ce904096be75c36395c1e2a6", "IPY_MODEL_648ae7464c72405289652955946ce953", "IPY_MODEL_7fc37bb70d3e471494eac537c2514cbc"], "layout": "IPY_MODEL_42769c6042ec447e839d1c2a0502838d"}}, "2545cbb1295b4cde9d459eb4a3f19691": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d7f07e9e2b49493e854fa4bc5842fa72", "style": "IPY_MODEL_1f7a030bc6194fb18673d76ac7878c05", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d7f07e9e2b49493e854fa4bc5842fa72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f7a030bc6194fb18673d76ac7878c05": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3aed222ad6b04da390e498eea952f956": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ffbd635603394f05858003cfa4d91d2a", "style": "IPY_MODEL_cbd96550729e45799338d60575ace3c4", "value": "<div>\n<style>\n    .QRGFIQ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .QRGFIQ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .QRGFIQ-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .QRGFIQ-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Brightness\"</b></h5>\n<div>\nTotal number of outliers: 1\n</div>\n<div>\nNon-outliers range: 0.2 to 0.74\n</div>\n<div class=\"QRGFIQ-container\">\n    <div class=\"QRGFIQ-row\">\n        <h5 class=\"QRGFIQ-item\">Brightness</h5>\n        <div class=\"QRGFIQ-item\">0.19</div>\n    </div>\n    <div class=\"QRGFIQ-row\">\n        <h5 class=\"QRGFIQ-item\">Image</h5>\n        <div class=\"QRGFIQ-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwAAAgMBAQEAAAAAAAAAAAAAAAUBAwQGAgf/xAA7EAACAQMCBAQDBQYGAwEAAAABAgMABBEFEhMhMUEGIlFhFHGBFTKRodEjQlKxwfAzQ2JyguEWJDTx/8QAGQEAAwEBAQAAAAAAAAAAAAAAAAECAwQF/8QAJREAAgICAgICAgMBAAAAAAAAAAECEQMxEiFBUQQyEyIUgZFC/9oADAMBAAIRAxEAPwD5jRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRSAmpqKKBE0VFFABRRRQBFFFTQMKKKKACiiigRFFFFMYUUVOKAIooooAKKKKACiiigAooooAKKKmgCKmiikIKKKKACiiigAooqaAIqaKmgCKKmigDxRRU0DBRk17K1ZaxGRicdK0G3OOlRKaTo1hjbjZhIqMVqaA+leTCefKmpol42Z6K0GBsgY7ZrxwmJwBzp8kLiyrFFeiMHB61FMkiiiigQUUUUAFFFFABRU0UARU0UUAFFTRQAUVOKnFIZFFTRRYFdFFFMRot7kwIwC5Jqw6hKegFY6Klwi3bRoskkqTNXxspPRevpR8bJz8q8/as1FHCPoX5JezUL6UHO1emOleob1o5ldkUgHpislFLhH0P8kvZpueHM5kh+q1mr0pKkEcjXrYZCDGpJbsBnnTXXQpPl2VUU5Xw1qhjLyQCHCbsSMFJHblSu4t5beUxzIVYGhST0JxaVlVFTiiqJIoqaKAIoqaKACiippDIqaKmgAqaBU0gDFFTRSGU1FTRVkkVNGKnB9KAIqaMV7RGcgKCSfSkM81dDbSzDKrhR1ZuQFXLBHbrvuebdo/1qqe5knOGOFHRRyAqeV6KqtlyQWCNi4upG9eDGD/M0+0BYLS7iu9JuHd2kMbcaMBkUqAe5B+9+Qrla6vw+Bbw2zZAZjuJPz5Vjnk4QNMKUpaOmaSOOS/m1ESSrboHlKMBsDHHfqeRwO9LfE2hRlIwm39oA0cvTIPcill9p8l1rT3Mkw4TlWdATzI7fLlT2VgdN4skuXTO1T2GP17VyTzJJcNnVjg23y0cTfeH9SsrngvbmTJ8rx+ZW9xWO5sLq0OLi3kj/wBy132ja9I80c6bJUi8hV+eRjln3pn4hntr8xywjlIg3K47+lbfymtma+Mm+j5NUU48R6bHp96hgAEUqBgoOdp7ilFdcJqcVJHLOLjJxZFTRRVEgBTMaTJ9mPdswGwZ2+1UaZbia5Bf/DTzMa2XF21ybiKMkR45KO9Y5JStKP8AZvjgnFt/0KcUYrVDYXE0qxonmbGMnFe7zTLyyZFuIWUuMrjnn5VfJezLizJRU4x1opiIoqaKBlFFTRVkD+yubbTNFtrr7OtLqWaR1czqWxjGMdhUjxFZlSJPD+nMT3CAf0pe5L6BF6RXJX8Vz/SsAGTyrNR9mspVVHTW+p6TeTrH/wCMwMzYGI5GT8MEVruLzw3p7KsVjOWcAkwznI9Rk9KVWpGk6S12Ri5uQUhz+6vdv6Ujzmo4cnvofPitdnTK/hG4kLTLqkRLjmXDDH4H++lMh4b0M2y3KtfGNlDA57Hp2/KuOtEV513nCrzJ+VdZY3hv9qTSGDTrfLOOhb2Hvz+lKaa6TZUO1bRj1nQbSOBJNK47uesbAkkfUA56VptYHhiEcqENGpBTqQR1+g9acw6hDqd8xWIx2sMe1SCMgdsfWkl6/wAGk95Nj9u+FRR1Ht6Dl9awyd1FuzbHGrZvn2ZQRvv5ZJxik+t6lw4vho3y5HPn90UtfWJ9hWECPd1bqaXMxYksSSepNGL4tS5SFk+QqqA00C6EN2YnbCyjA+fauqSUHMcpJXGevQ1wkcUrDdGrEA9QOlO7G7ucASHzjl5hS+Vh5PkmafEydcWhjqtkmoWoCMOOoyN2Ru9vlSOfw9qcLwoLcyNKm4bOePY+9MLi9+zdWRYyXieNGAc52kjmM+ma7C01GKCxja6iXjyFWi2c9igdPqacJSwqm+iZxWZ2tnzG6tJ7OdoLmJo5F6qapxX0W6aPVX1S5S2CuYF2oMDLFsBfwpJoPh5UaO81YARhd6wE4Z+Yxmt45002zCWBqVIWOvwOmKn+dN5m9vQVt0XTTJhWXDdXJ/lT7VLKwvr03rngcIAwwRrkH2P1xWO3uhB5FTvzPfNceXM3D9Ns6oY6ffg6E+ELX4ZLiS8WElMhT1NZNb0q2s5IozfbjFzQ/eI79BWK51aWaMJz6Y59cf0rBv3HdISc/nUKfWivxu7kxhf6WniNFNtBFbXaDLEYAlNcPcQS20zRTIUdeoNddYXs9rdq6NuQc9h6Vl8UW0Er3N2rYbdvXl94NzHL6/jmujDmd1Iwy4ltHLUUUV2HIVUUUVQjfAGk0W6TtHLHJ+OVqzRrEXV0DKdsKDdI3oBXnTQz295bqueJEG+qsP1rbqjjTdPTTYjiaTz3BHUei1nNv6ryXFeWLtVvfjbrco2wxjZEvoo6VjAJOB1qK9IzIwZSQwOQR2NWlSpEN27GNlbpEsz3quvlHDA7tnOCPlV/GkuBtAWOIYwmfzrzxfiYt7SjdIdzZ6lueSffmfpWmH7zJborZHmLHPLpj3H/AFWDdu2dcVS6PMl2yQiGJmWMHcSvUms99dSXeQQdgA2g88Af2a3y6d8NpTXErZZsMiHv71hgTiDDEdCWPyqevsXfVCmirY4Hk8w5L61qt7FZJVQ7mJPRa3c0jljilI6CGa10/wAK2sRQtLOWmlYemRtH4H8aqW1WZFnhO5GHJlPL5Gl13Oo2hyoAXAz6fKsf2jNA+bSV4z3Yd65HjlN3HZ2LJHEqZv1+xKPBLFIZC0QLLtwU5nHz5c6a6VdvqzQRqxNwoVenTHc+1c/b6m+4C6ZnA5BjzIppY69FY6h8TEq4dTG4QYBUjB+venOEnFRktChkim5xexzaNJF8WgdRxJ9pB6eUkDHt1qrWrm90ky/FcPi7vLgdRjlUTatHpXw9yipNHKG4bOmTg4bmD3ya9al4i0rVbOFLkIXhUsu5TyPT64HQVkoeWmaOffTQhhk1TUrlZ5JXVM59AfkO9NrmUu8kr4DHqBypXqHiDd+zsI+Eg/ePU+9ZrS/MsPAmY7s8m9aueOcldUiIZIRdJ2zfDfI6MhGXLeVcdR351pAbO5l25GFFL7eEvdBmXA7U7S3DNn0FY5eMX0XC32ym3TCsHzlvTtVOupv0tnjzmJgHwf3T0zV95fQacuXwzEEBRzpXo1017c3drOx/9mMsvfDKD0H+3I+lVhjJv8nhE5JL6exFRUzI0Urxt95GKn5iivSPPK6lVyagUx0mxa9uljBAA5knoAKUpcVYRjbo3aXAbO1OouQCG2xqf3z1P8hSOaV55nllYs7ksSaZ6xqK3F8iW3K1gIEajv6mlTDDEe9TjT2yptaRFFFa7SyaYcSQ8OEdWPf5VcpKKtijFydIs06GSTIjXJJwPan9tBHZqTLIisevcj5elJJNSWGLgWKcNe79zU293xeZbEvUk9z7H+lcuSM596R1Y5Qj+u2O7u9tDC8bZkfbtUHsaS3EvCtCAebeUcsGpZkjBeZxk9upNZrn9pDxQfKD5frSxworJK062VNK6Qoi8gRnIq/Sg0l3uJJCKSTnp71ml/w4z7Vqssw2c9x0JG0VvLqPRhFXPvSM95LxbhiOg5Cs9STzqK0SpUYyduwr1GhkdUHViFH15V5rZo0MdxrFnDKSI3mUMR2GetNulYl2MvFe+O7t7eVWSWOLLoexJ/QCkOaZeIrr4zXLqYMWXftUn0HL9aW1GO+CsvJ9mTVtr/8AQnzqmrbY/wDsJ86ctMUPsi9L24tpm4b9+h5irm1m9KkCQLn0FYZv8Z/nXio/HB9tFPJJNpM9ySPIxZ2LE9yassrlrO8huU5mJw2PX1H1HKqKK0rwRfkbeJLVbfUjJFjhTqJIyO4PT8sUVdOwvvCsMnWWxfhvz57D0P54oqMT6r10Vk3fsSxoXYADJp5cP9laUIUIFzcDJPcLWPRVj47TTfciG6qpJW1LU90hO1jyBPQelRL9pU9I0gqj1tl2n6PJdWzXErGKMqeESOTsDj6D3q7V/D19YS84xIu0EmM7sEAA/nTm0jcwgRZOwFlXPTAphALi6lD3IK5ADsM5cE8utY/yXdmz+PFKjkrTS3ClpoZZJeW2CNSSfnisuoS3LTcK4jaEx8uEVK7fpX0NLa8k4q6bIwukiXh7JCgBJzz9Rj5fSuQ16HUFjPxzRuytlm7g9MCrxz5O5bJnCo1EQUUUV1HIFTk4xnl1xUUY5Z7GkBoZC8EOOpJFbdQjNtpkURGCzeYHsf7/AJ15sI1a04rf5Un9Kp1W9kvbqSWU+dpCWxyHz/nWPcpJejqtRg5e0YaipqK3OUKbeGwRqvFCq3Bid8N/tOPzIpTXQaFFBHoWsXc+Qxh4UJz+8f8A9FZ5vo176/00xfZMQsxdi7dWOT9aig9aADnlzNaGZbLCI4YXz5pFLEegzgV6s1LXUajqTWjWbU2V6lsww8UMYf57cn+dUaeSL+HH8WKhu42WupqzxcjbcyD0aqs1deZ+Llz/ABGqKqOkKX2ZOaKKimSNdCffNNZscLdRFP8Al2P40UugfhzI/wDCcmiubLDJyuD2dWGWNxqZv1Cya23vaszW7HmD1X2P61XpDBb5c/vKVH9/Suu0rSle9cmTKu2VQkYxVGp6fY2V40qpGcIQmxduWPf5CslnTi4M1/BU1KP+Fmm3LW0qzpJsKEtnGeWK3nXLXWLqK3hfitPCTKUBBU9ADkds9By5e9K7WNDE28th1+7nrkV5sre30kPcxvw8Dm7NnA9BWEZxSae/Bvlg200N5PEH2HLNM8Z47KELZyWPXyjoOWB15VxOsa3eavcNJcuApOQijAH6/OvGs6j9pXzzhSqEkgHuT1Ptn0pfXfjx0k5bPPy5LdR0TRRRWpiFPtAutP2LZXsacOQsZHcAkem306CkNbNPspb6fgwLl8Z+nrUTrj2Xj+3R2S6Tp0sV1ZaU4E0qrIkTEkHH+r1NcNcwTW9xJDcRtHKjYZGGCDXXT2UmlWkGrwSlpLRk4v8AECSQPzGM/KvBvLLxOXTUNkVwMiK4UYZR23DuP75Vhjm0uT0b5YX+qOOorbqemXWl3Rguo8d0cc1ceqnvWLFdSafaOVqgprEXj8MXBVgFlu0RhjrhSRzpVimMgZdBt028pbmRgcdcKo/WlLwVHyL63aJZSahq9rbRRs5eVdwA6LnmaxMpU4YV3fgzbonhfVPEEseZGHBtwe5/TOPwpTlS6CKtnMeKZON4m1F/WdgPpyrJpYB1K3z/ABivF3PJeXctxIBxJWLNtGBk1607y6hCfR6NQoNyI1DHx8+Om81nq28O67lPqxqmqjpCeyaKiimImioooA76KVY5F5jcPwHzpNr107W7O2dzsEDdx3IrXKdhZSRxEOCM86T68rqYOYMe0kEdyeteXgxr8is9f5M6xtoottZvraIRJIroOgkQNis93fXN6wa4lLY6KOSj5AVnxVkcEkn3ELfIV6KjFO6PLc5NU2VVNbLfS765k4cVtIW/2kV02neCpyFkvM9fuClLJGOwjCUtHHKpY4AJPtW+y0i9vHxHA4H8TDAr6Xa+GrWJxIkKA49KYfYbv1m2D/SKxee9I1WFLbOA0/wbPM6NNKq+bG0c8108XhpbS4idCIpYwcEAcwadWugmCficeRscxk96zeIbS5kRZYbnZJGCfmKynKUtmsFGPSMn/j6SW2oW887yLdRqxOehRhjFJo/DFpYyLNFIwljOVLpvX6jvTvTH1CHSwbjY9zcSYRQf8v1/v2rfHBOz7pDtA/dxmpUpJdMdK7aOVS4urWB4Ly2TULAkkB4uSe2O30qiPTPC98EeMSWjyHAiZ9wH9QK7tI84CR/icVRdaDpd9MHmtU3gEbozsJz3OOppqTWmDUXtHLL4Ksjh1ZmQ9GRtwr1ceFQ621rkiK2DshHU7jk5p1L4au7L9ro99IcdIpDjP1H9ahtTmtJTx7cOgO2WWQFgHHbI6Unkkn2OMItOjnZ/A8txcIUkCpjnnvXUfZME+iWek3DPFEibsjkC3MEGrYNVtJTsX9lxV8kmcgEdv+xV5Qagu2WRX4bg4Q/eUjPMfPNDm2tk8EmcsnhLQ1uOGszh3BKZfkO31rKPAcsMwnt7pJtpyAeRr6AbG0uIlR44yq8l9q9vaIF2o8QA9QDTUsgnw9Hx/UPCuqQSM7Wz7Sck4yBSw6Tfh9otZWP+lc192VJVPlA2d9pyKplitZH2zW+xm6Moxn61os0kuzN44tnx268L6tAin4NmyM+Ugmllxp95bKGuLWaMHoWQivsTadIdfaJmnNq1vlX7Bs9M1TO0llK9vdQi8sSSCxXnH6gj059qazyW0N4YvTPje0+lFfVz4P0PUoZktmjgYsGR1zuQdxg0Vqs0WrMZY2nQh16zEepK6n/HG4g9AR1pFJay3Gn8GQqHjfylj2r6Bc6QmoQrxSw28wVqi08LRcTFxO5X0VQM/WuPm1o76UlUmJPC/hbTbqQfFSSTP1I27UFdymkafaQtHDbRryx0rMNKsobeSOCAKAuGYMdx+tL99/pYUy77mxzgkt50+vp8/wAabnJ7IWOP/I8gWKFAu1AR3rVGqSA+cGkUdxaiNrgSfsz1EnIofQg9KWx6nd6o8gsZRDCowvL73zpIGjsVtYQecv51Vd3UVlA8pmVgnUE865GKO9EhS5ueXYLzzTC3tYFYuu5znDHaT/OnaFxfsbw6wLiLKwtGTjmwrNeGa6kj4du0hDcySAKrEUnkeNZJM5yjsFxWpIzImXiAbptL9MdOlDtoEq0YVi1OCRistrubu33lX0Fem+Pc/wD2Ih/21LpdBiXtN5Jx5ZBUIsqniSQTAnykKQeYqaXoq2XiLVcg2+oRkekkX6Vek2rRFEmSzuSR0yV6fQ0sudcsreFJHD5Q/dKMD9f1rbFcmSKCZkl3N5khA8xyO/timiWjaLlggd7SSJ+wSQEGlunXPw0U8d1HEiySPkA8m54/P86rnGpXcyqyi2tQ2H2OBJj29K9fZ+iLuWeOSYtnJkmJI+WDihK+w6SoS63Hpgc/Z7bd5DSW+fKT/EPQ0tF5cwSK0YkboCqnr279Kc6onh3ThEzyRw7SNqwS5ZwPUDoffNeUXjxxPZy6fHIZN7G6KsxA+6Mr/P8AGnwbfZXNV0E3iQ29xAXCKm0iRD13LyKkHpnsf51bfaukMIvhLcxxFQV4RDK2f3Sp6Hvy7Z9CKw33ha1njWSPWoFm2gSB2DKW9jnOM+tbNJ0TULJyIL21njUkEhjuB9sg/pT4pBaZt0fUUvk3b5RKzErvUIpHpjoaflZZPvwnp1JJBrjdS0nxNKDGL6WZCciNmWMt+HI1nuNU1ayiS0ube5gh5bnXc6kHrz7fLNDXomr8nZGaILwt7QPJlVk3ZGfn+teIxe8JZpXVs8iEc4HPt6/9mlWhyeHJ24a4FzyOXcjJ9Vz39utMG0+703e2mOLi2+98PITuUnqVPp3x86ONqyW0nRfMbZZlW5gCs5wrjr+PI0VY8cV1p7QSOqyhRkkg7WHQ+wz60VLT9WNU/NGP4qFHWM9+gFVyyXMytJYxncDtAcYFFFJFvoyQWWpglrm5AUnmqjrV2owyyWwQStGDyJoopDsSS6TjyRNxEA8wfkDXiK+it5BbS2piCqMCMhg7Z5gYoooqtFp8tmyC8uIztj0toNx5PJJhj/xxmtyx3RccS9Kf6VjyPxoop7ZBn2ay87JBKzopxuQo3P39BV8Nrrg+8wUgYGWUlvf+VFFNJESm0Z0XxHYzndaT3ETPk4KZ/nUpf6oWZbiBbVQQV4rftIyRjoOvLn759QaKK1lFRVoiE3J9ml72a8gVIlFyysMMMABhy+VYJX2Tt9pa1b2hYZMMUgLD6+p+VFFZraNUvBZbWumvHHPBb3d2wXlJcuVVvfHf16VguJpJZTaaTZ2e8ctyRk7P+Rooot2Jo0af4Tmim+IvrgvKDyBiXH5/Wukhhuk8onlwOmFUA/gKKKG22RfRefiQAXmOefLaKhRKWOeEcjOWiH9KKKffsXgrN1HHIRKyoqjHlkOffkenStsM8MqZhmRgfb9KKKiOR3Rc4LjZD2tu5LSWyNnOSEDZ+vWq1ghG027PGoGMA5APuKKK3yJWkYxbqwuYZWXehjMozz6bvbNFFFQ4JjU2j//Z\"/></div>\n    </div>\n</div>\n\n<style>\n    .VCIPZQ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .VCIPZQ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .VCIPZQ-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .VCIPZQ-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 8\n</div>\n<div>\nNon-outliers range: 0.22 to 0.55\n</div>\n<div class=\"VCIPZQ-container\">\n    <div class=\"VCIPZQ-row\">\n        <h5 class=\"VCIPZQ-item\">Mean Red Relative Intensity</h5>\n        <div class=\"VCIPZQ-item\">0.15</div><div class=\"VCIPZQ-item\">0.21</div><div class=\"VCIPZQ-item\">0.61</div><div class=\"VCIPZQ-item\">0.63</div><div class=\"VCIPZQ-item\">0.66</div><div class=\"VCIPZQ-item\">0.67</div><div class=\"VCIPZQ-item\">0.7</div>\n    </div>\n    <div class=\"VCIPZQ-row\">\n        <h5 class=\"VCIPZQ-item\">Image</h5>\n        <div class=\"VCIPZQ-item\"><img 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\"/></div><div 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\"/></div><div 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\"/></div><div 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\"/></div><div 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .DUYTBY-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .DUYTBY-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .DUYTBY-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .DUYTBY-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 11\n</div>\n<div>\nNon-outliers range: 0.23 to 0.49\n</div>\n<div class=\"DUYTBY-container\">\n    <div class=\"DUYTBY-row\">\n        <h5 class=\"DUYTBY-item\">Mean Green Relative Intensity</h5>\n        <div class=\"DUYTBY-item\">0.16</div><div class=\"DUYTBY-item\">0.21</div><div class=\"DUYTBY-item\">0.55</div><div class=\"DUYTBY-item\">0.55</div><div class=\"DUYTBY-item\">0.66</div><div class=\"DUYTBY-item\">0.72</div><div class=\"DUYTBY-item\">0.74</div>\n    </div>\n    <div class=\"DUYTBY-row\">\n        <h5 class=\"DUYTBY-item\">Image</h5>\n        <div class=\"DUYTBY-item\"><img src=\"data:image/png;base64,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\"/></div><div 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\"/></div><div 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n</div>"}}, "ffbd635603394f05858003cfa4d91d2a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cbd96550729e45799338d60575ace3c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a5a926f7ce904096be75c36395c1e2a6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_361a5fd92eef459daf40175ac51f0415", "style": "IPY_MODEL_369d4b3744474550a0530f0ef684cbb7", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "361a5fd92eef459daf40175ac51f0415": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "369d4b3744474550a0530f0ef684cbb7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "648ae7464c72405289652955946ce953": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_955e5f230794482fa285c80731e5ec1c", "style": "IPY_MODEL_9a0cc033b5494c60b38823782138b5a3", "value": "<style type=\"text/css\">\n#T_25747_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25747_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25747_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25747_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25747_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_25747_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >More Info</th>\n      <th class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_25747_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_25747_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, RMS Contrast, Mean Blue Relative Intensity</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "955e5f230794482fa285c80731e5ec1c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a0cc033b5494c60b38823782138b5a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7fc37bb70d3e471494eac537c2514cbc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9b0eab132dbe42379e5ba929780e1dc1", "style": "IPY_MODEL_e147c4af87594ff1b4910421b5b74fe1", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\" style=\"font-size: 14px;\">Go to top</a>"}}, "9b0eab132dbe42379e5ba929780e1dc1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e147c4af87594ff1b4910421b5b74fe1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42769c6042ec447e839d1c2a0502838d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "96c59b901c3d4245be70e169e0594c5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7f312f6a72504cb68720870e8d185a18": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5460a52e557047ae8044ded48aadfb02", "style": "IPY_MODEL_ae52550f07a6403eb87d107bcdc15c85", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "5460a52e557047ae8044ded48aadfb02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae52550f07a6403eb87d107bcdc15c85": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "219467a0065a4f3f9a9635ac60a61cb9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_b4a69ca1336a47b0ae04464bc644b296", "IPY_MODEL_f226b078fc53423fb11908d7274ad7a8", "IPY_MODEL_6a1e0ce0e4504c1a87f42388b89ade07"], "layout": "IPY_MODEL_128b46cfae724ff0a88a87b7b0ebe3c7"}}, "b4a69ca1336a47b0ae04464bc644b296": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aaef8cbef51c4161820d0e0ba1a95696", "style": "IPY_MODEL_dbfd45e67d9c48d9af0f5a1fe141f60f", "value": "<h4 id=\"ImagePropertyOutliers-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\"><b>Image Property Outliers - Train Dataset</b></h4>"}}, "aaef8cbef51c4161820d0e0ba1a95696": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dbfd45e67d9c48d9af0f5a1fe141f60f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f226b078fc53423fb11908d7274ad7a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f38a0d37e63544ee8a2c59a2192fd744", "style": "IPY_MODEL_20353d2177c0402e9ff36b0e16e535cf", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "f38a0d37e63544ee8a2c59a2192fd744": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "20353d2177c0402e9ff36b0e16e535cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6a1e0ce0e4504c1a87f42388b89ade07": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_155ed095cc2748928768c950c9276fc4", "IPY_MODEL_f3d50a4f72074c0ab40ca1d7126062f7", "IPY_MODEL_08ff922c104347bcbd8b5803b349b2fb", "IPY_MODEL_f1d4e5fa2d734c13acd4f79597c883a4", "IPY_MODEL_5c592efefc2f45859189b6be9ca10492"], "layout": "IPY_MODEL_21cc0b2c8fab4c959ffb7bce139049cb"}}, "155ed095cc2748928768c950c9276fc4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6275d0d95cb246fbb34bedf6d3ff991f", "style": "IPY_MODEL_f2114f62e6a94ea1b50b90ce3d13c81e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "6275d0d95cb246fbb34bedf6d3ff991f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2114f62e6a94ea1b50b90ce3d13c81e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f3d50a4f72074c0ab40ca1d7126062f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_87646cedf44c473db315591b00745146", "style": "IPY_MODEL_69aa27d711504ca1844be79abb249a73", "value": "<div>\n<style>\n    .KRVQBU-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .KRVQBU-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .KRVQBU-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .KRVQBU-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Brightness\"</b></h5>\n<div>\nTotal number of outliers: 5\n</div>\n<div>\nNon-outliers range: 0.21 to 0.75\n</div>\n<div class=\"KRVQBU-container\">\n    <div class=\"KRVQBU-row\">\n        <h5 class=\"KRVQBU-item\">Brightness</h5>\n        <div class=\"KRVQBU-item\">0.76</div><div class=\"KRVQBU-item\">0.78</div><div class=\"KRVQBU-item\">0.79</div><div class=\"KRVQBU-item\">0.82</div><div class=\"KRVQBU-item\">0.91</div>\n    </div>\n    <div class=\"KRVQBU-row\">\n        <h5 class=\"KRVQBU-item\">Image</h5>\n        <div class=\"KRVQBU-item\"><img src=\"data:image/png;base64,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\"/></div><div 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CTGglBD4YnA8gsG4S3j/AMHbE+J/FkGTijG02d4aSfMd0kF3sPZVtF8MTa1p0uRO8wEuHklw+8B97Za7E8N6Niy+ZHhRuf1WDJ8VfIFdcAAVQDRwEXL8gnTy/VNDyNGmbBNIyRs0ZLC32K473OZ0uaC17XWHDtS1vjXUcfKz8aLGeJDAD1FvqTxf0WSmcGZEkbL6L2KnKasrowu8dNL4dxsrxJqL8nU5Xy47Pvt6qBPYUtmNG0qg393423/ALKfs/wBQjjbk4ErmtJd5rC40D2P9FuPT+ad36xvukMGDh45qDFhZ/wCrAFNwKRkC0Egtpo0ex91AIclFXqq2G2ePHazIkEkg5cO6sjdKgxNcpHcJymJoWeCkDE7JDcoQben2q0wfuog3pLjd2bUgNlM8FAVpeoWdygDiTQaTY9VO4OA4QN+F1EFUSN7+n6D8wkgyWnoto29O4SQF8ECrRAjsotgO6MXQFq6SRPQ9EI45T3ukZiKG26VXXZEUh7JAnFsbHPkcA1oslxoALEajruX4i1L906H1Mx7qWcfiHc+w/mux4nLs9+PoWO6psk+ZI7syMck/M9ld0/T8Pw7pDzGwVFGXyyVu8gWqnROB4o0vD0nQMaOBrQftALnHlx6SLWS1WKOLyOkAgMouHfuLVXUdUytUzn5WTI5wc4029mjsArGpSiXTIXF9uGwo9vdFkssb8d0p30wEgkH27LraTq2r6TGzM63z4TzTmPNg1yPYrixyMfEYgSXu5I4C13gWeLJOVo2YwSRSt8xocO42P6fyRPEZ+7bbS87H1LCZlYzrieOL3ae4KssaRG0Pd1OGxdVWsNiGXwd4h+yzucdMyz8Lj+H3+i3RPWAWEEc2OCpyiCoVskD7WkB62kSB3+SgyJI5CBzwQbqhyndTgonN35v2RoFjTDJi81oIaeD6qXtXqoZJosVjGkfE4hrWN5KnQC4TE0OE53QkEclAA4+m1IH7kEduEdgbkfIITsRYBJVEqSNcbc4m29klPKNv5UkmE+x7lP7oRv3+nolTvw+qsknIsJw4dQbe6EHlN0NdIH+iRpQbTuc1rS5xDWgWT6BCFw/F2ZLFpYw8Wzk5zvJZXYHk/klJukreFnu1TVtS1uQfA93kQf8AoP8AoV3xpOYPC2b08vaGfmV0NJwWadpsGJHVRsAJ9T3K5vjPHdleGcksNeVUpaRyAdwiX+wZJugti8Bvz5WXNIWSD/iy1x9bwY4cLGezp6aI6m7F/fdepY+LBm+HI8Rjg6GXGDA4enTyvHdTjnxMmTEyetroXFpa48Kt9aXjd3tWxiGStH3Re5Wy8GiJviaHypLqN/UeLFFYbzek2Oynx9WmxMbJdEakyGeSHDkNuzX5Ulh16rk/x69q0Wk+IofsIzoftDHWzoeC5jv6peGNQdkYsmBktDMzAd5UrfUDhy8UhkmhzGGN7mvBBBBogrWaX4jmxtdxtTyCSXgQ5RH4xxZ9/wCyr56Y/T0vVdWxtMMLZ+p0k7umNjKslI6i9tiXT8thuhTA79QVnmGPW/2hFzSJMbToQ4VuC7t+p/Ra88KLJFRzn587wfJ06cm6t9MCjEeqTO+KWHEb/wAB1u/M7LomjzsnoBoIFhLZq+Lgw48hlJfLMRRlkNkj+itg1v6oTxtymNluwv3UgdqN72hp6jQCLcAC0L2NcwtIBDgQQiBH1dW429E/bdItIPKTuDSogOIJSQuBJscUkmEgdvfZFY6rtCQK5O6TRuN/8qySN3JO3oiIobBA4tjaZHO6WtBJPoAl1AgWd3fd90jG3YcWVRmaJtfxgQCIIXyb9iSAP6q60EWT9FSxGuk1nNyLBjDWRNPuNz/MJQOkChniZkQSQybskaWOHsdk/JRBSGT8DZckIzNFyT/EwpCGA8ltrpeJNE0jVMV0moeXBJVNyCelzf7rn6lgnA8Z4eqNBGPmEQyn0fW357LIeOMrOz9SynkOOLjSGBgHAI/qtJN9xNumY1fTn4WVMyHIiyYWHaWN2xXOjaXvaAOO1qR0buRt80h8A2/+S6CrRXLYsd/VqDXEWOq6XdZDFPmPg6gxkpG5/Da4Wm2M6M1e+66r5gyeU0A1rT8SvHztN9bz9mWOG4eoTlwdIZhETd7NH9yttf8AlY/wPg/uuLFaeoOzsLzng/7g7+xWvI53WGTWGJFbphuE57BDYo1vRUmfc9k42BtMLrfuntIGO+x3SJNbAWl34SJpAMf1Qm+E/dDI3rb0mxfoqILgb4SRVskgB3O57pwKAIKXv7IirIWxFOFgjcHui7qMc3ujG5vukBtO6qaWC2GbqaA4zyE/mrQIQwsMfXbgQ5xI24SNJZHA5/RP1AJidkBdup0EWfix6hhux5CWk05rhy1w3BC8l8VN1PTNWnGXuJ3eY4D7rz/uC9faT6e6z/jbSm6ppjH7B0LhvV0DstMLrpNm3kebqMU2KxjMdrJQd3rmPe57rJ3XW1jRMzS8kw5MLmmzR7H3BXM8p1nbhPK2+lJIeCQxyBwO4XQxI3ZORHD1WJXgO+VqlDjvkeGtaSSdqC9W8I+EMfHxMfNz2OORfmNZwAO1hLZ6aLPhbjSYGWyxHi/w3AD8LgB+myv/AIzd1SeVrJY3RPFtcKI9lUxosmB7YHSCSFjfhefvD0B9VNqpFsjdN8+U970h3BUGe9kwSJTHb5pgR4Q3umLim5KeiMd904TH2T3sKQCKSYEnchJAMyzzwj/LZICxaYgKyPfcom3SjI2onnunYb+SQSg7ehStReY9pbbBZ+9R4RkpGTzsD6JNcCLCAuFchK99gjQHdXZ2QygSQvY4bPBFLka54iw9F6WZBdJO4W2Ng3Pz9FxWaz4o1h16ZpzMSHtJL/c/2Romnm0+HNxRjahCyYBoFkce6wuo+FcCTUmYWjZT5Z3O/iNABbE3uXOXci8N6pmUdZ1uaRt7xQmgfqtDp+n4mmweVhQNiZ3rcu+Z7qt6F7U9K8NaVpDvOxoOuSq6n/Efp6LrB9gGiPbuh6gHAWT3KIkXQ3UXdM+31KZM4nqHCZtk2Tz2SAvog39NlIhB2QDE7IbTu4I7+qHtSZlaQAHdLe6CaufZMhJkimQD2kh4SQEt2K2TEUeUklRGsUh6t623SSSA2GzRT7EmjZCSSDQubvQqk1kHYnflJJAc3UdKE2pQ6nDHFLPEzp8uUWHD29HK/jZbMqO4+prm7PjOzmH0ISSQFtm437eia9zaSSQA34nkn4fQI3ObGC57gGjukkkCI22FpmkUfZJJAGSOyYjayQkkgBIs78JqSSQDd7H1Tjf5JJJgj3TUkkgEQkkkgP/Z\"/></div><div 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\"/></div><div 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IiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg/9k=\"/></div>\n    </div>\n</div>\n\n<style>\n    .LHUPOA-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .LHUPOA-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .LHUPOA-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .LHUPOA-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"RMS Contrast\"</b></h5>\n<div>\nTotal number of outliers: 3\n</div>\n<div>\nNon-outliers range: 0.08 to 0.34\n</div>\n<div class=\"LHUPOA-container\">\n    <div class=\"LHUPOA-row\">\n        <h5 class=\"LHUPOA-item\">RMS Contrast</h5>\n        <div class=\"LHUPOA-item\">0.07</div><div class=\"LHUPOA-item\">0.35</div><div class=\"LHUPOA-item\">0.37</div>\n    </div>\n    <div class=\"LHUPOA-row\">\n        <h5 class=\"LHUPOA-item\">Image</h5>\n        <div class=\"LHUPOA-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"LHUPOA-item\"><img 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\"/></div><div 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BuFXLUxjywtyB2CpDJph6SFSsKVvYbLHdE55wAt0yhcRuFcZQgHkiOfFG93RF0wpGgckQ1noiLbmojlilLhFIx5Y7S7S4HSex7FHyxxuY2SRjXSHSwOcAXHngd0RRvx7wiljnjEkMjJGHOHMcCDjbmFWSAMk4CIicplxDXBwBaQQeRClEVZoiIgIiICofDE85dG0n3CIoKmsa0eUAfCYREEOYCMELBlstumeXyUkbnHqWoimSrOVnpb/AOP2zUHCjjBHssoW6kbyhaiJkPKrwgiaMNY0fZVBgAwAiK4ajAzjCq0hEUwRpRETDX//2Q==\"/></div>\n    </div>\n</div>\n\n<style>\n    .IYTATJ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .IYTATJ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .IYTATJ-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .IYTATJ-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 14\n</div>\n<div>\nNon-outliers range: 0.22 to 0.56\n</div>\n<div class=\"IYTATJ-container\">\n    <div class=\"IYTATJ-row\">\n        <h5 class=\"IYTATJ-item\">Mean Red Relative Intensity</h5>\n        <div class=\"IYTATJ-item\">0.13</div><div class=\"IYTATJ-item\">0.16</div><div class=\"IYTATJ-item\">0.18</div><div class=\"IYTATJ-item\">0.2</div><div class=\"IYTATJ-item\">0.6</div><div class=\"IYTATJ-item\">0.63</div><div class=\"IYTATJ-item\">0.65</div><div class=\"IYTATJ-item\">0.72</div><div class=\"IYTATJ-item\">0.83</div>\n    </div>\n    <div class=\"IYTATJ-row\">\n        <h5 class=\"IYTATJ-item\">Image</h5>\n        <div class=\"IYTATJ-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"IYTATJ-item\"><img 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n\n<style>\n    .HFAHQJ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .HFAHQJ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .HFAHQJ-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .HFAHQJ-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 15\n</div>\n<div>\nNon-outliers range: 0.21 to 0.52\n</div>\n<div class=\"HFAHQJ-container\">\n    <div class=\"HFAHQJ-row\">\n        <h5 class=\"HFAHQJ-item\">Mean Green Relative Intensity</h5>\n        <div class=\"HFAHQJ-item\">0.16</div><div class=\"HFAHQJ-item\">0.17</div><div class=\"HFAHQJ-item\">0.17</div><div class=\"HFAHQJ-item\">0.17</div><div class=\"HFAHQJ-item\">0.18</div><div class=\"HFAHQJ-item\">0.55</div><div class=\"HFAHQJ-item\">0.57</div><div class=\"HFAHQJ-item\">0.57</div><div class=\"HFAHQJ-item\">0.58</div><div class=\"HFAHQJ-item\">0.59</div>\n    </div>\n    <div class=\"HFAHQJ-row\">\n        <h5 class=\"HFAHQJ-item\">Image</h5>\n        <div class=\"HFAHQJ-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"HFAHQJ-item\"><img 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NLaui2d2o8kgZHUDnBOWP3FGux/6SoY5y1V2fjzIURyBGF0DPJLjP9qh5TqNk3Kj2hX+xVciZYwhBw24B2FLzbStBI7+XbGSeK1z2RZTJKpWMAqNI1HjOTk/Osq8ii4MIBYcZO4Py9ajjJyRx5JJjrovSkmspopgCCmlCP4XGNP9fsKFubURwpIRiTUUeMnGCAMN8jn7g0d02CdIHkmkMY2wvG44J+9MfxAqKk0ToDJPH40ZGMFwPOPkQD9hTIjcOTq6MlkayjDDjsahFWQgXTRxs6q5IUO5wMHjJ9RXixuPKwJIyDt3HNVwnemdSGS3TOF2NeXEAni07Bux9KuljCMMZ+tRabXwHOKkqYnhjeO8VHXBBpvCC0oArrQpOrAyBjNWWSZmJPAoYw6sHi4ViTS/YZBb5lArQ9N8jAUv6PGLmWVhwpwKcRQ6JhVCWgs+RNOI0AzH9KldqMR1KGzlJmG6T1bxrA2lzIQUBK57/KqbmTSuCDqPYUBHCAoNW5I3zUTwXsqyfxsZy7J0VSyykkgaVxQJupLSYSxnJHxA8MPSiZ3LA0DNGXNYodfAf6yxfh6ajo9/aXTFkkwyrnQ2x/5ppBOss0iAjA2G/NY21twoG1XSBkdZI2KupyCOQasjkdbRd1k43L01sn8R9KV3MhU7Hc71z0/qFzdv4cixgckgVLxVRSxYk0fZNWhmIkc5Mbn0FLi2W1Hk1akpWJ1zswzj3qkb0uTsOxpM+bCLHrRXQnRfERxkvkoPfA/waU+IWhVPQ0Z06eKDw5nyxjmRl0+jBs/ap8240R8r8KGvWy4jCKCdRGCBsMikFv01471JAMkfEccVqbgeJ006uYZyobPBB2z/AOeldTeB+XNw4XxjhWXbGNhmoY0nRyMgst4fzcpAIGlDq08cc1f1C3mvY/17Yaow/hqwyoOPJt65/tQ8d5Fb9WQgJuvnI21ZHGfemfW54h05upW0qvGyqmA2PMdvvsu3sadFC72Y02OuCd4sEQIrjJ3YEgD+orh3ladrjxMykh842b6elMulM7J1EygFfy2lTj4vMuKEigDSEcYOM4+1NR08cuxJp4ruHGnQ6/D/AHFCAGjJLIxa2fIw2OPbnNC96qi7Vl0do6XivFkMYZVG5rpRXD7OaJhLRpPwjv4wPqK0Wjzg1m/wiw8aZflWnxhqZHw5/IdZGXNtHUqud8RmpSmyeEG0YJ4dKjFDkYNMpsadqXud69PR3q0DOlciMVcw2rilAdFdnS7V62CK4B3qM2lSTR3SCelbLunXQi6nDGSAsjaST2plfR5U6u1Zu0zL1GN/4UdSfvWr6kASxHHNewbg2yfC25N/sRtsDXsZwCPWo+MnHFRRtXhx2KcdNsENtclzp8OMS+Yb4KbbD/5Cldsoe4jUnAZwCcZxvWg6fv067LnTpZY2ckbqmBp29QpNJyekXKk00kE2zSXdjL4uFaVWJAGxY7ik7TFlImRysbjUB3AO4/bFF9OvWhumibZDIdse+Qf3I+ooPrc3gXEkcaYEucD0J2/rUWS4sgnDbKZYCLtip1HJ8o+Fh2Hsd6ButZAKS7McPzvjcZ7Z5q+IyWsmS51Fseuc+v7UvnvBMxtoo9OWySO3r+9bFt+Cowt0Fo7RFSpxjfHaiYb0RJIvgrmXSGOfQ54obBIHrRlv02e4t5JkU4QZ45rpRgqSZ2nCNLsNvysU1izQAyxMuT6oazUkeiRh70z6d1KTp/iRkakYce9L5nMsjO3JOaOlR6CcW0/CsVVMdMo9CKuUb1Xdr5UPoa8/Bgw6Bci26iuThXGK18lyu29fPo2Ixg7jcUyTqM0joHJArYuhWTCpuzVXU4/Ls2e1Sk91c/6IgHkVKXJ7PYsKSFUsuVwDXECa5N6rOobV3byeG+9ebtljQRcwKI8gUtYYpncTBo9qXac0DMSK15qi8k0rpFEsNGTQSqbm6C8gUubb+1Cc8vIr5LLJNEZJ5IrTdUOmIDO5UUrsrUS38UJ+HOW+Qpp1U5cjGMCq8cesKPRVSUUJO9dKdq8YgscVOKWGEWi67mNfU096esq9PujMsfhMHlZBtl9KDGPrtSKwlWG9jkYnSuc4OOxrQ30hNsy7qFIzn+LA0sR7YKfak5PTncu+6Qos38QhWYa12ye9NI/DvEHjonjxDAz3pCuVc74OacnEccEjEB5RuPXFb9GOSFSG9IyjsQdXgvVkZUhY5OBjc0HN0XqNrY/m7mNoRL5VLcj3PpW1td5QTv6Zo+dFuoGgmGY2XSR7V6HGpeiHjUZJsVdP6MiaGmkEo0jBHDbc1oI41WMKqgD0pP0iOS0H5KZtWg/pt7U8j22qpeWDyJSumzI9esRa3erHkkGR7Gk4ONq0H4mlVpkhZyzL7YA/5pCV3obT8LsVuCbPBzXFyM27Y7b1b3qMBgg8GtoYLIZMtitZbdNim6aGA8xGc1kPDaK7Kb4zt8q2/wCHJhJZ+GTum1DidtpgZZNRtCKWV1BifldjUq/8QQ+BfkjhxmpS56kGpJqy+8stB2FANEQeK1N1GsikUpkg83FMcTcWXstgBi8lDAYY04kiwlKJfIWNLlodGSasDvZdKkVf0qALEZW5ald05lm0jjNNoXCQKgPal4ty7EuOXfI5MZ9NUJM87gb+VM96q6nIWkAXfJ3NUCU+GGUHAAG3Y15Jq8Ji2x7VW3qhy9sF9SPWva98NlUMRse9QgjYgil0YQc5rSW2mTp5vJP9vwwsh9cLpI+p4FZtaIh8aRfBjLlSclATjPriglGyfNi+pR1EjTzqijJY1b+KWaKK2SN9Lx5IP2p10qwWAFnwZSNz6Uk/Fqax5W+E4+tDnfTH/wBJuRkSjQb0DqQv4denQ0ZAk32ye4p8sgZip2PNfOOi3LRC5iB8sijP0NajpPUjL+hOx18Rue/sabiyXFX6Fji8mNTY/bS+6nzDg1fb3IJ0yEBx+9LEuNM5RjxTFY1Yg98b01oDJBJUwfqHTIbx/ELHOCQO2r1oAdEjWBvEbMvIxxTsYxgVCuo777VkYJGRzTiqsxEkRRypG4NcMMr71ousdODI00WAVGSPWkM0bxxlmUgY5rfC2E1NWgCQK0inuKa/h648K/MZOzj96UIcnJqy2lMd2jrypzSVL7rGSjaob/ix/wDUxL3AJqUN+IplnuYipzhN6lbkVyFxVRSNDDMJVz61zLFvnFSpTPgB/bKkVzJ+nWW6nII3ZalSp870H2axsVR7vqo2J9qlSgx6R6Go6C7eUK/nz4bfEP70d1FAIIyg2O9SpVMfByAo5CGQNugOcUdeBJkM0RBAAz7VKlavDDm1ginljOjZuwp7FbxxqRGgX6VKlFFCcraLkYrjHxHYVl/xV+hcyoeTyD61KlQc53KKObyXtCXosImvWQkhQpOfWnF1pVVCDAHFSpRY/EdDg/4T23u5AdLMWY8En9q2lm2q1VjuSN/nUqVZAzlr7UemQEHFVQXaM2k7EVKlNaERgmmLutdYto4QiTKxc7hd+Kzd51CS4h8MDShOfc1KlSzk+1F2KCjGkCqcLUiwJM1KlDH0P5JO7O+W+lSpUpgp+n//2Q==\"/></div><div 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\"/></div><div 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\"/></div><div 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\"/></div><div 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n</div>"}}, "87646cedf44c473db315591b00745146": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69aa27d711504ca1844be79abb249a73": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "08ff922c104347bcbd8b5803b349b2fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4135efd9720f45debb596fe56b23d3bc", "style": "IPY_MODEL_5d58857d25b54c5896141ecfdd8d31e8", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "4135efd9720f45debb596fe56b23d3bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d58857d25b54c5896141ecfdd8d31e8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f1d4e5fa2d734c13acd4f79597c883a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1d50e74f37b94171ab6a8083cb267348", "style": "IPY_MODEL_4a824643b55741818186daa5c050e941", "value": "<style type=\"text/css\">\n#T_16ed0_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_16ed0_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_16ed0_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_16ed0_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_16ed0_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_16ed0_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >More Info</th>\n      <th class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_16ed0_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_16ed0_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, Mean Blue Relative Intensity</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1d50e74f37b94171ab6a8083cb267348": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4a824643b55741818186daa5c050e941": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c592efefc2f45859189b6be9ca10492": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_73fe0ea970aa43d0b1d8979c694cef46", "style": "IPY_MODEL_a791f07b4df74e648b1797e797995ff0", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\" style=\"font-size: 14px;\">Go to top</a>"}}, "73fe0ea970aa43d0b1d8979c694cef46": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a791f07b4df74e648b1797e797995ff0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "21cc0b2c8fab4c959ffb7bce139049cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "128b46cfae724ff0a88a87b7b0ebe3c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0efced4ded4743ca872b73abdb863399": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_27a9d78ce14842c28eebb2273e0a4b15", "IPY_MODEL_5db05022d01b401ab66a8a6dd5e3ebb9", "IPY_MODEL_7e2f3f932ad14159a14a61cebfec0ffd"], "layout": "IPY_MODEL_b70b11acb22b431c86bed6984a81fa9e"}}, "27a9d78ce14842c28eebb2273e0a4b15": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8217087f597749b4bc94b365a4d1a776", "style": "IPY_MODEL_f66e3050149b41eeba9de555a7e4fd3a", "value": "<h4 id=\"PropertyLabelCorrelation-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\"><b>Property Label Correlation - Test Dataset</b></h4>"}}, "8217087f597749b4bc94b365a4d1a776": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f66e3050149b41eeba9de555a7e4fd3a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5db05022d01b401ab66a8a6dd5e3ebb9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4cf7912ba5df490e9e28fb55e37f1ea2", "style": "IPY_MODEL_112a458253c540aa9fd9f350a8787f9b", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4cf7912ba5df490e9e28fb55e37f1ea2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "112a458253c540aa9fd9f350a8787f9b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7e2f3f932ad14159a14a61cebfec0ffd": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_659c3f7fb3f145e0b4efc8aae8ab1434", "IPY_MODEL_fb735187afa54b1dac913f7656597476", "IPY_MODEL_44f1581d40204db4bf2383c80a6386ca", "IPY_MODEL_b7de429d67b7447686949111bf52ec67", "IPY_MODEL_b70fc477cdd142dda78a00886889316e", "IPY_MODEL_ec086c8c34644d83bc9ca21dc89668ef", "IPY_MODEL_c62f1c7507de4001ba8d04970707e59c"], "layout": "IPY_MODEL_882f5ce1b77c4e57b2bbb8a55b4f50fa"}}, "659c3f7fb3f145e0b4efc8aae8ab1434": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e1c5f22a5ef4dd890e45de9ada966ed", "style": "IPY_MODEL_4858129ed9b7460ab9686fa8984a1ed2", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4e1c5f22a5ef4dd890e45de9ada966ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4858129ed9b7460ab9686fa8984a1ed2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fb735187afa54b1dac913f7656597476": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#69b3a2"}, "name": "Test", "text": ["<b>0.08</b>", "<b>0.06</b>", "<b>0.05</b>"], "textposition": "outside", "x": ["Brightness", "Mean Green Relative Intensity", "RMS Contrast"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "4b86db3d-5ed2-486d-9bd5-7fab08b53767"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, 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"closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "6cIoaIrJtD8H+U4qqDesP0RkNqX2Aaw/"}]}, "44f1581d40204db4bf2383c80a6386ca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bb27cefe34574a53a6fabab332c7c985", "style": "IPY_MODEL_50cccc2b9bbb492cb0d45e71a6588993", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/en/stable/checks_gallery/vision/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "bb27cefe34574a53a6fabab332c7c985": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "50cccc2b9bbb492cb0d45e71a6588993": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7de429d67b7447686949111bf52ec67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_385f779209d642bc947de8b3364731b1", "style": "IPY_MODEL_9b6bbca86dd24820a3edbd6f87d00f07", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "385f779209d642bc947de8b3364731b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b6bbca86dd24820a3edbd6f87d00f07": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b70fc477cdd142dda78a00886889316e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_63312c43c547428eacf199643afba114", "style": "IPY_MODEL_8418ec5afcf4466baea5a6e8a74c42dd", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "63312c43c547428eacf199643afba114": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8418ec5afcf4466baea5a6e8a74c42dd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec086c8c34644d83bc9ca21dc89668ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb1466812d2d4d488db7b1def0a0550d", "style": "IPY_MODEL_6e53d89165fb46d1a9192d8f7debd642", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "fb1466812d2d4d488db7b1def0a0550d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e53d89165fb46d1a9192d8f7debd642": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c62f1c7507de4001ba8d04970707e59c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8f10cf90d0804ed88eaf89f15bdba813", "style": "IPY_MODEL_26252b63428949048bf74989a55d3b88", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\" style=\"font-size: 14px;\">Go to top</a>"}}, "8f10cf90d0804ed88eaf89f15bdba813": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26252b63428949048bf74989a55d3b88": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "882f5ce1b77c4e57b2bbb8a55b4f50fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b70b11acb22b431c86bed6984a81fa9e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b309e1bc8ef54842af42881d800bb7ec": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_4eb3ecca258f48438189c76ecbccaf6c", "IPY_MODEL_8f0da96536f74cdfa76eafc4e4222d67", "IPY_MODEL_944f7ae8ae174a5c821573287a010d8b"], "layout": "IPY_MODEL_1c03e6839733413c8e9fa3c3483b4f3c"}}, "4eb3ecca258f48438189c76ecbccaf6c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c1b022fcfec14184b453207580edc498", "style": "IPY_MODEL_c634c78b11c740489f6c8cc35602515a", "value": "<h4 id=\"PropertyLabelCorrelation-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\"><b>Property Label Correlation - Train Dataset</b></h4>"}}, "c1b022fcfec14184b453207580edc498": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c634c78b11c740489f6c8cc35602515a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8f0da96536f74cdfa76eafc4e4222d67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a70836ed4d494b4993eb7b3297e9a810", "style": "IPY_MODEL_884a9c78496747109e395ad6aa5fd558", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a70836ed4d494b4993eb7b3297e9a810": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "884a9c78496747109e395ad6aa5fd558": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "944f7ae8ae174a5c821573287a010d8b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_bdc35a0ed9c44b2aa0a93767e86e7601", "IPY_MODEL_e2c08479aa73468c93c418a79885acb2", "IPY_MODEL_e1a5f4826a9046888341c7561967acc4", "IPY_MODEL_1364a49699c349dcb72efd7089fbe8e7", "IPY_MODEL_85f264f1616e407491f502f1f322d9cf", "IPY_MODEL_3b6e920933b74e36be484d6be7ea68e7", "IPY_MODEL_c222669659e848b5a97f652876779e8d"], "layout": "IPY_MODEL_35450ff3790041e78f7f4e40cdfee9e9"}}, "bdc35a0ed9c44b2aa0a93767e86e7601": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cd85ae29d5ce47878a00864d95530187", "style": "IPY_MODEL_99841ec0efc740d591257fc3c05172b3", "value": "<h5><b>Additional Outputs</b></h5>"}}, "cd85ae29d5ce47878a00864d95530187": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "99841ec0efc740d591257fc3c05172b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2c08479aa73468c93c418a79885acb2": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.1</b>", "<b>0.07</b>", "<b>0.06</b>"], "textposition": "outside", "x": ["Brightness", "Mean Blue Relative Intensity", "Mean Red Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "6896b16f-e424-42b7-ab0d-56a431d22011"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": 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[{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, 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"landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "VchOYlq/uT9VwRpxuhSxP8O7HMAi064/"}]}, "e1a5f4826a9046888341c7561967acc4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b64229a3bf09483aa8b1ff33a4cc7f5e", "style": "IPY_MODEL_61fb7524fc204b69b8a548f2e3272185", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/en/stable/checks_gallery/vision/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "b64229a3bf09483aa8b1ff33a4cc7f5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61fb7524fc204b69b8a548f2e3272185": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1364a49699c349dcb72efd7089fbe8e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e09b3564f6964765b5302c6150f6795e", "style": "IPY_MODEL_7c687d5d1ab04a599f5360f0e4d0b47d", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "e09b3564f6964765b5302c6150f6795e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7c687d5d1ab04a599f5360f0e4d0b47d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "85f264f1616e407491f502f1f322d9cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d4503a9704d54595956b3e4982ce54f6", "style": "IPY_MODEL_63a5b525936241cc8b73a8544d955c8f", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "d4503a9704d54595956b3e4982ce54f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "63a5b525936241cc8b73a8544d955c8f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b6e920933b74e36be484d6be7ea68e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d86a0044c04e47448556b0abaf0dea4c", "style": "IPY_MODEL_95207336d08a49c8855b0e64d1ae0178", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "d86a0044c04e47448556b0abaf0dea4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95207336d08a49c8855b0e64d1ae0178": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c222669659e848b5a97f652876779e8d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e14c4fa773ae4cfba1f93edf3abc3dc5", "style": "IPY_MODEL_7378960be7d44291a4a91a3fdbb041c5", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\" style=\"font-size: 14px;\">Go to top</a>"}}, "e14c4fa773ae4cfba1f93edf3abc3dc5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7378960be7d44291a4a91a3fdbb041c5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "35450ff3790041e78f7f4e40cdfee9e9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1c03e6839733413c8e9fa3c3483b4f3c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "320a3904f4ce4a28b46ab07bfa8ffb60": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_88fe583742674cf984da586b3577543b", "IPY_MODEL_b7e99dd810ac4707a5f297971c58d36c", "IPY_MODEL_124fca4eaa85494ca06e1aad8ee73b57"], "layout": "IPY_MODEL_23896f0eb01445b3a52bc88629bad950"}}, "88fe583742674cf984da586b3577543b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_71a85a83750b4ae2aafc77789dea7060", "style": "IPY_MODEL_af1917c6219742e4a4f25285ade52523", "value": "<h4 id=\"ConfusionMatrix-TestDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\"><b>Confusion Matrix - Test Dataset</b></h4>"}}, "71a85a83750b4ae2aafc77789dea7060": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "af1917c6219742e4a4f25285ade52523": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7e99dd810ac4707a5f297971c58d36c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5bdc6ae889db4a3f832580d04ab9fd52", "style": "IPY_MODEL_0e06b745b1f64f55aef70cae7f483792", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5bdc6ae889db4a3f832580d04ab9fd52": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e06b745b1f64f55aef70cae7f483792": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "124fca4eaa85494ca06e1aad8ee73b57": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ef4ee2d2f36a42a6bd1c62615893a727", "IPY_MODEL_5467c15d64fd4461a6c62af5d884ccfd", "IPY_MODEL_70b494befdfe47b5bd55578bc1908455", "IPY_MODEL_c2f66268afeb4fd89c9280a8f759e38a"], "layout": "IPY_MODEL_dbb96d84c6a74210bcd3c60d60d1bc69"}}, "ef4ee2d2f36a42a6bd1c62615893a727": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c49d8cf2bc84a34b84c513d50b98065", "style": "IPY_MODEL_f355dde21e044898aae362e0086e644e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7c49d8cf2bc84a34b84c513d50b98065": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f355dde21e044898aae362e0086e644e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5467c15d64fd4461a6c62af5d884ccfd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c5fe2f6b2a44954a336fb9dbd5f190b", "style": "IPY_MODEL_c54134d8f93f4196acc1801d318792b7", "value": "<div>Showing 10 of 2 classes:</div>"}}, "9c5fe2f6b2a44954a336fb9dbd5f190b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c54134d8f93f4196acc1801d318792b7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "70b494befdfe47b5bd55578bc1908455": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"colorbar": {"title": {"text": "% out of<br>True Values"}}, "text": [[68, 2], [80, 3]], "texttemplate": "%{z}%<br>(%{text})", "x": ["ants", "bees"], "y": ["ants", "bees"], "z": [["97.14", "2.86"], ["96.39", "3.61"]], "type": "heatmap", "uid": "f9e9dc6e-49c9-4f21-bab0-01255049da02"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], 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{"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "c2f66268afeb4fd89c9280a8f759e38a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_496c06af7924424a8c84535e0c3776ac", "style": "IPY_MODEL_0886d2dc04c74c778dbaf33aee8ea390", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\" style=\"font-size: 14px;\">Go to top</a>"}}, "496c06af7924424a8c84535e0c3776ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0886d2dc04c74c778dbaf33aee8ea390": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dbb96d84c6a74210bcd3c60d60d1bc69": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "23896f0eb01445b3a52bc88629bad950": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "78db1ec609f4493499c938dcda1b2910": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_9296455f3deb421694df914b4a72b49c", "IPY_MODEL_8e654df54a2d4635be72d393fccb9b96", "IPY_MODEL_983311186fcd493bb1f0989479b10366"], "layout": "IPY_MODEL_8bf3208baaad431cb9da248fda03099a"}}, "9296455f3deb421694df914b4a72b49c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c7eaf67f66704d4db35f978dab006c44", "style": "IPY_MODEL_c3abfe48221746649d6d44d73d22956d", "value": "<h4 id=\"ConfusionMatrix-TrainDataset_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\"><b>Confusion Matrix - Train Dataset</b></h4>"}}, "c7eaf67f66704d4db35f978dab006c44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3abfe48221746649d6d44d73d22956d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8e654df54a2d4635be72d393fccb9b96": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dcbfe9bb902e4ed585362bdc7b0be083", "style": "IPY_MODEL_f507fb7307b74b49a87d21eecf38cc46", "value": 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"_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "65d95305d07d4583b593487af4c14716": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b461a012bcdb44e680ca64f65106dfff", "style": "IPY_MODEL_fc74fa6067e84f728bd1a83efcc8fff3", "value": "<br><a href=\"#summary_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\" style=\"font-size: 14px;\">Go to top</a>"}}, "b461a012bcdb44e680ca64f65106dfff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc74fa6067e84f728bd1a83efcc8fff3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "916086eefbf842bf8138cb7d52e71886": {"model_name": "LayoutModel", 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"<h4 id=\"HeatmapComparison_YR8LNOEMQGY27VYLIENFNVUPR-section-8RHNU\"><b>Heatmap Comparison</b></h4>"}}, "aa1fbd405aa247129fe9149c3f3e626f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "acebeac193ad46d5b4fbbc26eab645c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8273129df80b421d8222001c9b484ac7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8463e65518b946ddb6ab03aff50a6f44", "style": "IPY_MODEL_9a78637622834cdba2c3e1689a2ef5b8", "value": "<p>Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/vision/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8463e65518b946ddb6ab03aff50a6f44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a78637622834cdba2c3e1689a2ef5b8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f45cb990ac654339b40377243166e80a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_12bc5d4dfd734608ab909fc72f5a891f", "IPY_MODEL_2bacfa3f9c094b49ae57806882eddf0c", "IPY_MODEL_a731b92ef7e94ebc95d3542f16855138"], "layout": "IPY_MODEL_cd4dfb5e12c643edb32bb8fb8fefc765"}}, "12bc5d4dfd734608ab909fc72f5a891f": {"model_name": "HTMLModel", 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.. _sphx_glr_download_user-guide_vision_auto_quickstarts_plot_classification_tutorial.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_classification_tutorial.py <plot_classification_tutorial.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_classification_tutorial.ipynb <plot_classification_tutorial.ipynb>`


.. only:: html

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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_