.. 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/index>`

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).

.. GENERATED FROM PYTHON SOURCE LINES 19-21

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

.. GENERATED FROM PYTHON SOURCE LINES 21-41

.. 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 42-45

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 45-51

.. 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 52-60

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 60-134

.. 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")








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

.. GENERATED FROM PYTHON SOURCE LINES 138-160

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





.. GENERATED FROM PYTHON SOURCE LINES 161-168

.. 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 168-176

.. 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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    100%|#########9| 44.6M/44.7M [00:00<00:00, 95.7MB/s]
    100%|##########| 44.7M/44.7M [00:00<00:00, 93.3MB/s]




.. GENERATED FROM PYTHON SOURCE LINES 177-185

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 185-198

.. 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([[[ 0.10831,  0.10831,  0.09119,  ...,  0.81043,  0.84468,  0.87893],
             [ 0.22819,  0.19394,  0.17681,  ...,  0.77618,  0.82755,  0.86180],
             [ 0.26244,  0.26244,  0.29669,  ...,  0.82755,  0.82755,  0.84468],
             ...,
             [ 0.22819,  0.15969,  0.15969,  ...,  0.72481,  0.75905,  0.81043],
             [ 0.15969,  0.15969,  0.14256,  ...,  0.70768,  0.75905,  0.81043],
             [ 0.10831,  0.10831,  0.12544,  ...,  0.70768,  0.74193,  0.81043]],

            [[-0.30252, -0.32003, -0.32003,  ...,  0.29272,  0.38025,  0.41527],
             [-0.23249, -0.26751, -0.26751,  ...,  0.31022,  0.34524,  0.38025],
             [-0.23249, -0.21499, -0.16246,  ...,  0.29272,  0.32773,  0.36275],
             ...,
             [-0.37255, -0.40756, -0.37255,  ...,  0.59034,  0.57283,  0.53782],
             [-0.44258, -0.42507, -0.39006,  ...,  0.57283,  0.57283,  0.53782],
             [-0.49510, -0.46008, -0.40756,  ...,  0.57283,  0.57283,  0.53782]],

            [[-0.53211, -0.51468, -0.53211,  ..., -0.11381, -0.04410,  0.06048],
             [-0.47983, -0.47983, -0.47983,  ..., -0.13124, -0.06153,  0.02562],
             [-0.46240, -0.42754, -0.37525,  ..., -0.07895, -0.06153, -0.00924],
             ...,
             [-0.56697, -0.58440, -0.54954,  ...,  0.42649,  0.44392,  0.44392],
             [-0.63669, -0.60183, -0.58440,  ...,  0.39163,  0.42649,  0.44392],
             [-0.68898, -0.67155, -0.61926,  ...,  0.39163,  0.44392,  0.47878]]])
    --------------------------------------------------------------------------------
    Second element is:  <class 'torch.Tensor'> with len of  4
    Example output of a label shape from the dataloader  torch.Size([])
    Image values tensor(0)




.. GENERATED FROM PYTHON SOURCE LINES 199-205

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 205-244

.. 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)








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

.. GENERATED FROM PYTHON SOURCE LINES 246-257

.. 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], [1], [1], [0]]
    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.jpg
    Visual example of an image. Label class 1 Prediction class 1
    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], [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 (1).jpg
    Visual example of an image. Label class 1 Prediction class 1
    validate_extractors can be set to skip the image saving or change the save path
    *******************************************************************************




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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 264-270

.. code-block:: default


    from deepchecks.vision.suites import full_suite

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





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    |###################                    | 19/39 [Time: 00:02]



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    |################################       | 32/39 [Time: 00:04]



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    |#################################      | 33/39 [Time: 00:05]



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    |###################################### | 38/39 [Time: 00:05]



    Ingesting Batches - Test Dataset:
    |#######################################| 39/39 [Time: 00:05]




    Computing Single Dataset Checks - Test Dataset:
    |      | 0/6 [Time: 00:00]




    Computing Single Dataset Checks - Test Dataset:
    |####  | 4/6 [Time: 00:00, Check=Image Segment Performance]




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




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





    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 271-274

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

.. GENERATED FROM PYTHON SOURCE LINES 274-277

.. code-block:: default


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

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.. GENERATED FROM PYTHON SOURCE LINES 278-279

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.. GENERATED FROM PYTHON SOURCE LINES 279-281

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Read more about\n                <a href=https://docs.deepchecks.com/en/stable/examples/guides/create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n            </p>\n\n"}}, "a59ee0143bcb440b8d32c7f4812311c6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "416ac05e9e5642eb806676ba051bf222": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ddcdc4cebf004301a81e303c6ddee138": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3db3dbdf08a34d38824b214c582daf74", "IPY_MODEL_a95b5eb7ac3c48afb448ca91802c2390"], "layout": "IPY_MODEL_3c62f297e07f48c29b2331e97ed6651d"}}, "3db3dbdf08a34d38824b214c582daf74": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5ee3d53e24414728a4cc71b7654a9e25", "style": "IPY_MODEL_5e65ef3f5fc5482f93be6e032bcde214", "value": "<span id=\"summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\"></span>"}}, "5ee3d53e24414728a4cc71b7654a9e25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e65ef3f5fc5482f93be6e032bcde214": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a95b5eb7ac3c48afb448ca91802c2390": {"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": "Didn't Pass"}, "children": ["IPY_MODEL_2141296c3e5d47eeb63cec01fedfd85f"], "layout": "IPY_MODEL_51dde5a4babf44dab9cd4871f1d48a0a", "selected_index": null}}, "2141296c3e5d47eeb63cec01fedfd85f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_92d2c4db2b344dcd9aa697cb4e68309c", "IPY_MODEL_4bea27185ea0423fa49d41505480b3a3", "IPY_MODEL_6246f61dbfb44f6e93bfbaa1e361186c", "IPY_MODEL_3806c2bd31b64d55ac0ad574c3f1b550", "IPY_MODEL_c04cf4a9d83d45d7a7a97870d54100cc", "IPY_MODEL_3806c2bd31b64d55ac0ad574c3f1b550", "IPY_MODEL_f68a7d40187a416090d1c8a4bf4df63e", "IPY_MODEL_3806c2bd31b64d55ac0ad574c3f1b550", "IPY_MODEL_00f75fe834ca4d5387a3dde5fa6a06e7"], "layout": "IPY_MODEL_3694c34830f0476b837e33dde8151c25"}}, "92d2c4db2b344dcd9aa697cb4e68309c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b8f5d927642b4f86945171c4e610a2d2", "style": "IPY_MODEL_16a533f3a5e84d4682065bab6578bd82", "value": "<style type=\"text/css\">\n#T_118cf_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_118cf_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_118cf_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_118cf_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_118cf_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_118cf_\">\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_118cf_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_118cf_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\">Class Performance</a></td>\n      <td id=\"T_118cf_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_118cf_row0_col3\" class=\"data row0 col3\" >2 scores failed. Found max degradation of 70.83% for metric Precision and class ants.</td>\n    </tr>\n    <tr>\n      <td id=\"T_118cf_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_118cf_row1_col1\" class=\"data row1 col1\" ><a href=\"#SimpleModelComparison_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\">Simple Model Comparison</a></td>\n      <td id=\"T_118cf_row1_col2\" class=\"data row1 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_118cf_row1_col3\" class=\"data row1 col3\" >Found metrics with gain below threshold: {'F1': {'ants': '-161.41%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_118cf_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_118cf_row2_col1\" class=\"data row2 col1\" ><a href=\"#ImageSegmentPerformance-TestDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\">Image Segment Performance - Test Dataset</a></td>\n      <td id=\"T_118cf_row2_col2\" class=\"data row2 col2\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_118cf_row2_col3\" class=\"data row2 col3\" >Properties with failed segments: Brightness: {'Range': '[0.51, 0.57)', 'Metric': 'Precision', 'Ratio': 0.38}, Mean Blue Relative Intensity: {'Range': '[0.23, 0.28)', 'Metric': 'Precision', 'Ratio': 0.66}, Mean Green Relative Intensity: {'Range': '[0.33, 0.34)', 'Metric': 'Precision', 'Ratio': 0.43},...</td>\n    </tr>\n    <tr>\n      <td id=\"T_118cf_row3_col0\" class=\"data row3 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_118cf_row3_col1\" class=\"data row3 col1\" ><a href=\"#ImageSegmentPerformance-TrainDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\">Image Segment Performance - Train Dataset</a></td>\n      <td id=\"T_118cf_row3_col2\" class=\"data row3 col2\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_118cf_row3_col3\" class=\"data row3 col3\" >Properties with failed segments: Brightness: {'Range': '[0.37, 0.43)', 'Metric': 'Precision', 'Ratio': 0.43}, Mean Blue Relative Intensity: {'Range': '[0.16, 0.23)', 'Metric': 'Precision', 'Ratio': 0.45}, Mean Green Relative Intensity: {'Range': '[0.42, inf)', 'Metric': 'Precision', 'Ratio': 0.35}, ...</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b8f5d927642b4f86945171c4e610a2d2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "16a533f3a5e84d4682065bab6578bd82": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4bea27185ea0423fa49d41505480b3a3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_29e20cc4db4b4569b6355ad7f56bc82b", "style": "IPY_MODEL_d34d1b535c80414ba79043933726a465", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "29e20cc4db4b4569b6355ad7f56bc82b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d34d1b535c80414ba79043933726a465": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6246f61dbfb44f6e93bfbaa1e361186c": {"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_50b04107ac12412ea85a653e57a1b4da", "IPY_MODEL_8d9eb291baf04a9a8a259c857f25534d", "IPY_MODEL_13a60c79ed82456484a0f689163efc57", "IPY_MODEL_3b7ebb51f0994cfa82bc91e0b97507b0"], "layout": "IPY_MODEL_c43a81235f404c0c8e5f2f3106ee81fc"}}, "50b04107ac12412ea85a653e57a1b4da": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ae048cdaf384792b3dc8a04415c80aa", "style": "IPY_MODEL_25a30f54d108457ba8a69479620143f7", "value": "<h4 id=\"ClassPerformance_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\"><b>Class Performance</b></h4>"}}, "4ae048cdaf384792b3dc8a04415c80aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25a30f54d108457ba8a69479620143f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8d9eb291baf04a9a8a259c857f25534d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_71802193f13c4520affa8d4fb7d65838", "style": "IPY_MODEL_324d2795101d4419a09c594372827ff2", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "71802193f13c4520affa8d4fb7d65838": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "324d2795101d4419a09c594372827ff2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "13a60c79ed82456484a0f689163efc57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_663fdfff60954d9788cc3c8b117c7990", "style": "IPY_MODEL_21333f2c9ea342d78180c193ac823e65", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e2dcd_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e2dcd_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e2dcd_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e2dcd_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e2dcd_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e2dcd_\">\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_e2dcd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_e2dcd_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_e2dcd_row0_col2\" class=\"data row0 col2\" >2 scores failed. Found max degradation of 70.83% for metric Precision and class ants.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "663fdfff60954d9788cc3c8b117c7990": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "21333f2c9ea342d78180c193ac823e65": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b7ebb51f0994cfa82bc91e0b97507b0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e5d011cf252f425caa2a09af8ab8a4ab", "IPY_MODEL_d04a8238a1d24b83ac05860c232c537f", "IPY_MODEL_016a1dbd776b45268312ba8f6b284a8c"], "layout": "IPY_MODEL_4a24e7ba73ef44dfbb3094d6a67df546"}}, "e5d011cf252f425caa2a09af8ab8a4ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5478edf054f344e7813419f330a757a3", "style": "IPY_MODEL_27059158a6b240f49369cb808eb03edf", "value": "<h5><b>Additional Outputs</b></h5>"}}, "5478edf054f344e7813419f330a757a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "27059158a6b240f49369cb808eb03edf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d04a8238a1d24b83ac05860c232c537f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "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": ["bees", "ants"], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "fec1a1ce-b508-4874-aabb-eef2f67541c7"}, {"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": "25b8316a-389a-4295-9052-70a00451341b"}, {"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": ["bees", "ants"], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "bd5f4124-a308-49d1-bc2f-d992f0254b49"}, {"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": ["bees", "ants"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "histogram", "uid": "cad1acd0-3f15-43f9-859a-6a9ce94f4d85"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Recall", "x": 0.2375, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Precision", "x": 0.7625, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Dataset"}, "tracegroupgap": 0}, "margin": {"t": 60}, "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"], 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"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}}}, "xaxis": {"anchor": "y", "domain": [0.0, 0.475], "title": {"text": "Class"}, "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], 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{"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9d080c2ac5024564ab962cde7218916f", "style": "IPY_MODEL_3116a4aa52264a15b20c29b6e603f21f", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "9d080c2ac5024564ab962cde7218916f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3116a4aa52264a15b20c29b6e603f21f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c04cf4a9d83d45d7a7a97870d54100cc": {"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_72069b41fdb74c03aacb9468e12df3d5", "IPY_MODEL_dffd7f84187c4e48958867994eefcdea", "IPY_MODEL_8b2df52561224bba963c80d511466519", "IPY_MODEL_c3c6c915aa2e4d41af3da18334bfff83"], "layout": "IPY_MODEL_d3a333dc479f4308bb2ce42e89285670"}}, "72069b41fdb74c03aacb9468e12df3d5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e66b4bd4c8cf4b9c9f7552337b8ec1bf", "style": "IPY_MODEL_8ed90b41f7de4014a780bc04ec263bc7", "value": "<h4 id=\"SimpleModelComparison_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\"><b>Simple Model Comparison</b></h4>"}}, "e66b4bd4c8cf4b9c9f7552337b8ec1bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ed90b41f7de4014a780bc04ec263bc7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dffd7f84187c4e48958867994eefcdea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cce3e5bd4fb14acd98e23a3a31e27b4a", "style": "IPY_MODEL_0884d4478adc45248687e3b89eb20b17", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "cce3e5bd4fb14acd98e23a3a31e27b4a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0884d4478adc45248687e3b89eb20b17": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8b2df52561224bba963c80d511466519": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e091057b5e3a43b891669f02264f5a76", "style": "IPY_MODEL_ae41ca9a2d2746fcb209e405bab1d80f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_8dcf5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8dcf5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8dcf5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8dcf5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8dcf5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8dcf5_\">\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_8dcf5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_8dcf5_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_8dcf5_row0_col2\" class=\"data row0 col2\" >Found metrics with gain below threshold: {'F1': {'ants': '-161.41%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e091057b5e3a43b891669f02264f5a76": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae41ca9a2d2746fcb209e405bab1d80f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c3c6c915aa2e4d41af3da18334bfff83": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e285bbbfb37e4067a9b09de868fdcb23", "IPY_MODEL_b75f2a4eea204806bf2661811526ddd6", "IPY_MODEL_0a0d759efc0741bf9c900ddefafb709a"], "layout": "IPY_MODEL_f97e80a177e54b5485a94cdbf4da8a50"}}, 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"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": "dhphuacR5j+yusEU+aybPw=="}]}, "0a0d759efc0741bf9c900ddefafb709a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_24920512a4fd4a20bcc3615b05ef8c6d", "style": "IPY_MODEL_c25c3b96834a4493adde36c3ac6ab17e", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\" style=\"font-size: 14px;\">Go to top</a>"}}, "24920512a4fd4a20bcc3615b05ef8c6d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c25c3b96834a4493adde36c3ac6ab17e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f97e80a177e54b5485a94cdbf4da8a50": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3a333dc479f4308bb2ce42e89285670": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f68a7d40187a416090d1c8a4bf4df63e": {"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_02b1b2e6e2f545f2b92f8a9b1191452a", "IPY_MODEL_b8ecdccb8a6e4ab3974b2a38160a46e7", "IPY_MODEL_23bd30405f6b43b59006afa3094a95f5", "IPY_MODEL_f9d499481a414ecbab05a33d852b9e31"], "layout": "IPY_MODEL_9428aae316874e7ba59860e4ebe55b98"}}, "02b1b2e6e2f545f2b92f8a9b1191452a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_02ec9b9d69a64059b334ce7dd91eb7fe", "style": "IPY_MODEL_227ca235d1314f81b2a44b5823b39076", "value": "<h4 id=\"ImageSegmentPerformance-TestDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\"><b>Image Segment Performance - Test Dataset</b></h4>"}}, "02ec9b9d69a64059b334ce7dd91eb7fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "227ca235d1314f81b2a44b5823b39076": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b8ecdccb8a6e4ab3974b2a38160a46e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_121b4505c65e407da95328e1db4c60ee", "style": "IPY_MODEL_7f32041732234e8293b929c50fdb921e", "value": "<p>Segment the data by various properties of the image, and compare the performance of the segments. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "121b4505c65e407da95328e1db4c60ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7f32041732234e8293b929c50fdb921e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "23bd30405f6b43b59006afa3094a95f5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0439c4f7a7ac4c8db2fe8fe4ffed9bbc", "style": "IPY_MODEL_9d19f6486921404a8a558ff2f620c3cc", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b8b00_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b8b00_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b8b00_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b8b00_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b8b00_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b8b00_\">\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_b8b00_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b8b00_row0_col1\" class=\"data row0 col1\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_b8b00_row0_col2\" class=\"data row0 col2\" >Properties with failed segments: Brightness: {'Range': '[0.51, 0.57)', 'Metric': 'Precision', 'Ratio': 0.38}, Mean Blue Relative Intensity: {'Range': '[0.23, 0.28)', 'Metric': 'Precision', 'Ratio': 0.66}, Mean Green Relative Intensity: {'Range': '[0.33, 0.34)', 'Metric': 'Precision', 'Ratio': 0.43}, Mean Red Relative Intensity: {'Range': '[0.34, 0.36)', 'Metric': 'Precision', 'Ratio': 0.73}, RMS Contrast: {'Range': '[0.19, 0.22)', 'Metric': 'Precision', 'Ratio': 0.73}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0439c4f7a7ac4c8db2fe8fe4ffed9bbc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d19f6486921404a8a558ff2f620c3cc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f9d499481a414ecbab05a33d852b9e31": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b35240af91b54333bcfd5aaf9f038a9b", "IPY_MODEL_8839b30089cc49f9815616032fad6c4a", "IPY_MODEL_ce78d348253f43b6ac8cc53664f37393"], "layout": "IPY_MODEL_38fcb2668b954426aba7bb617b169ded"}}, "b35240af91b54333bcfd5aaf9f038a9b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c32e0afb3d0d4cd287daac30a387ac1a", "style": "IPY_MODEL_42faa9c9987446248a9401e097517aeb", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c32e0afb3d0d4cd287daac30a387ac1a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "42faa9c9987446248a9401e097517aeb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, 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"hBBCCCGE0D/Cck8jLPfUP9daa6211so/btu2bdu24T8AAAAAAADQPw=="}]}, "ce78d348253f43b6ac8cc53664f37393": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6a778ba102df42cf9c32763a849b3873", "style": "IPY_MODEL_e906ccc11479449284c3ede891a59877", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\" style=\"font-size: 14px;\">Go to top</a>"}}, "6a778ba102df42cf9c32763a849b3873": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e906ccc11479449284c3ede891a59877": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "38fcb2668b954426aba7bb617b169ded": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9428aae316874e7ba59860e4ebe55b98": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "00f75fe834ca4d5387a3dde5fa6a06e7": {"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_c3a5453954e14af1b4b4ac35b8d0eca5", "IPY_MODEL_3130e28a71c849a2bb6bf9829e8c76b7", "IPY_MODEL_b73cf7864f5b4e99a8e99dfde0bf8a18", "IPY_MODEL_18f34e05fb4a4bb9b36e607df28906fc"], "layout": "IPY_MODEL_a09bda3e2ed84c57b83310f15540fe6a"}}, "c3a5453954e14af1b4b4ac35b8d0eca5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d76fcd24749c4edd847e2771c8d13ae0", "style": "IPY_MODEL_fceed420a7974e1fbfbdea45c4c0a696", "value": "<h4 id=\"ImageSegmentPerformance-TrainDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-27VYL\"><b>Image Segment Performance - Train Dataset</b></h4>"}}, "d76fcd24749c4edd847e2771c8d13ae0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fceed420a7974e1fbfbdea45c4c0a696": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3130e28a71c849a2bb6bf9829e8c76b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5638e96989f649ae9539cadc6771b43b", "style": "IPY_MODEL_8a1ef009861a4cf2866599e55b30d671", "value": "<p>Segment the data by various properties of the image, and compare the performance of the segments. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "5638e96989f649ae9539cadc6771b43b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8a1ef009861a4cf2866599e55b30d671": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b73cf7864f5b4e99a8e99dfde0bf8a18": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_98fb98f3ef5e4b2786f17b2622666b9d", "style": "IPY_MODEL_7a2480fcbd8b4eb18cae6db3a082b4ae", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5b025_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5b025_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5b025_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5b025_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5b025_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5b025_\">\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_5b025_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_5b025_row0_col1\" class=\"data row0 col1\" >Segment's ratio between score to mean is greater than 80%</td>\n      <td id=\"T_5b025_row0_col2\" class=\"data row0 col2\" >Properties with failed segments: Brightness: {'Range': '[0.37, 0.43)', 'Metric': 'Precision', 'Ratio': 0.43}, Mean Blue Relative Intensity: {'Range': '[0.16, 0.23)', 'Metric': 'Precision', 'Ratio': 0.45}, Mean Green Relative Intensity: {'Range': '[0.42, inf)', 'Metric': 'Precision', 'Ratio': 0.35}, Mean Red Relative Intensity: {'Range': '[0.45, inf)', 'Metric': 'Precision', 'Ratio': 0.56}, RMS Contrast: {'Range': '(-inf, 0.16)', 'Metric': 'Precision', 'Ratio': 0.26}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "98fb98f3ef5e4b2786f17b2622666b9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a2480fcbd8b4eb18cae6db3a082b4ae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18f34e05fb4a4bb9b36e607df28906fc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a34f57a788674f13908b603552a8948f", "IPY_MODEL_fcfa9140d50e41f1b4d48b5722db8332", "IPY_MODEL_e73d7f84d2b242a39510a01c46bac994"], "layout": "IPY_MODEL_c0922d87201944ff9e07379483b97273"}}, "a34f57a788674f13908b603552a8948f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ba6069b317bd44738b4756c3d30f163b", "style": "IPY_MODEL_5f0093cdb3874dd8b9a97e516b090c81", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ba6069b317bd44738b4756c3d30f163b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5f0093cdb3874dd8b9a97e516b090c81": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fcfa9140d50e41f1b4d48b5722db8332": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", 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{"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": "VVVVVVVVxT/lCmJyBTHpP2JyBTG5gug/qqqqqqqq6T/f2ajvbNTnPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "ICZXEJMr6D9VVVVVVVXlP6S+s1Hf2eg/LIjJFcTk6j9VVVVVVVXNPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "OQUvp+Dl1D8AAAAAAADQP9/ZqO9s1Oc/hixkIQtZ6D9XEJMriMnlPw=="}]}, "e73d7f84d2b242a39510a01c46bac994": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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{}}, "51dde5a4babf44dab9cd4871f1d48a0a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c62f297e07f48c29b2331e97ed6651d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b6c998d7ee6e4ecabfacaad48493fbce": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d7a03cc8bb694e608146646f2768d3b4", "IPY_MODEL_8377cbf52b7242e480790acc1b9f04fa"], "layout": "IPY_MODEL_a99e159811a64fd39689c31a0e4dc21d"}}, "d7a03cc8bb694e608146646f2768d3b4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b550555fdc354f98974dfa8e788fd22b", "style": "IPY_MODEL_e00ad0e5ddd349a6af1fa674a8257d54", "value": "<span id=\"summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\"></span>"}}, "b550555fdc354f98974dfa8e788fd22b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e00ad0e5ddd349a6af1fa674a8257d54": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8377cbf52b7242e480790acc1b9f04fa": {"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_79f4a3a47f1e4772be20122b53cb7653"], "layout": "IPY_MODEL_5cdbdfd7bf25493d9c8db37ce15ef882", "selected_index": null}}, "79f4a3a47f1e4772be20122b53cb7653": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_36f84beed7dd44289f52673bbbc133b2", "IPY_MODEL_15b1f8aa0bd742ac84814543d18ea05a", "IPY_MODEL_7c38a2e671204a5ba2e3510b26e8a46b", "IPY_MODEL_1773157a5e7f471f9a8cc7b54ffe1885", "IPY_MODEL_3c6d8e77d21e45738f47af951503baa4", "IPY_MODEL_1773157a5e7f471f9a8cc7b54ffe1885", "IPY_MODEL_91fedbdb5eab47d99fc879f0d946bb6b", "IPY_MODEL_1773157a5e7f471f9a8cc7b54ffe1885", "IPY_MODEL_fc05ec0815f94ac48fd6fd93c7c557e6"], "layout": "IPY_MODEL_beaa70e722de4987ac42a1b0573bf976"}}, "36f84beed7dd44289f52673bbbc133b2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b2d4def73937461c94d0cfcdb633194e", "style": "IPY_MODEL_4d97c3f061e843aea718ffc8d3f3de82", "value": "<style type=\"text/css\">\n#T_df990_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_df990_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_df990_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_df990_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_df990_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_df990_\">\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_df990_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_df990_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestLabelDrift_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\">Train Test Label Drift</a></td>\n      <td id=\"T_df990_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_df990_row0_col3\" class=\"data row0 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_df990_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_df990_row1_col1\" class=\"data row1 col1\" ><a href=\"#ImagePropertyDrift_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\">Image Property Drift</a></td>\n      <td id=\"T_df990_row1_col2\" class=\"data row1 col2\" >Earth Mover's Distance < 0.1 for image properties drift</td>\n      <td id=\"T_df990_row1_col3\" class=\"data row1 col3\" >Found property RMS Contrast with largest Earth Mover's Distance score 0.04</td>\n    </tr>\n    <tr>\n      <td id=\"T_df990_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_df990_row2_col1\" class=\"data row2 col1\" ><a href=\"#FeatureLabelCorrelationChange_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\">Feature Label Correlation Change</a></td>\n      <td id=\"T_df990_row2_col2\" class=\"data row2 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_df990_row2_col3\" class=\"data row2 col3\" >Found highest PPS 0.09 for property Brightness and class bees</td>\n    </tr>\n    <tr>\n      <td id=\"T_df990_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_df990_row3_col1\" class=\"data row3 col1\" ><a href=\"#TrainTestPredictionDrift_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\">Train Test Prediction Drift</a></td>\n      <td id=\"T_df990_row3_col2\" class=\"data row3 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075 for prediction drift</td>\n      <td id=\"T_df990_row3_col3\" class=\"data row3 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_df990_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_df990_row4_col1\" class=\"data row4 col1\" >New Labels</td>\n      <td id=\"T_df990_row4_col2\" class=\"data row4 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_df990_row4_col3\" class=\"data row4 col3\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b2d4def73937461c94d0cfcdb633194e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d97c3f061e843aea718ffc8d3f3de82": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "15b1f8aa0bd742ac84814543d18ea05a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c2d632ed3294b5dbd04421acbcb22da", "style": "IPY_MODEL_667ec86264d0486d9f0b35e126a95322", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "7c2d632ed3294b5dbd04421acbcb22da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "667ec86264d0486d9f0b35e126a95322": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c38a2e671204a5ba2e3510b26e8a46b": {"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_c78307c681484a468bfbeba6c7d2e125", "IPY_MODEL_fa856da36a5149ffa6e3aaa23a1bee40", "IPY_MODEL_7223a986881f4774974c6341fcc6b97d", "IPY_MODEL_2744f4c5110449a8a46232ee4814207b"], "layout": "IPY_MODEL_5d49956b2eb948f69dc8a76b319b366f"}}, "c78307c681484a468bfbeba6c7d2e125": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_de90df7fdbdd4084a13413514b04deb8", "style": "IPY_MODEL_56623f1270724e01a4aeac829d40d486", "value": "<h4 id=\"TrainTestLabelDrift_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\"><b>Train Test Label Drift</b></h4>"}}, "de90df7fdbdd4084a13413514b04deb8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "56623f1270724e01a4aeac829d40d486": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa856da36a5149ffa6e3aaa23a1bee40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2c4bf333b08c4f43a093297e42bfc079", "style": "IPY_MODEL_c80d1ce9c8c94b529cb49caa8e411666", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "2c4bf333b08c4f43a093297e42bfc079": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c80d1ce9c8c94b529cb49caa8e411666": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7223a986881f4774974c6341fcc6b97d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b1b5a91d8514fea9249029d553ebe09", "style": "IPY_MODEL_8b6143ac07f44271826515222507de66", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f1fc5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f1fc5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f1fc5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f1fc5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f1fc5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f1fc5_\">\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_f1fc5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f1fc5_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_f1fc5_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"}}, "1b1b5a91d8514fea9249029d553ebe09": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b6143ac07f44271826515222507de66": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2744f4c5110449a8a46232ee4814207b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_13d5c6af4295427d8bdcb9e59c11a68d", "IPY_MODEL_d874ff100da24dcda2dc03e3652d7cd8", "IPY_MODEL_bc6e5dd410b34f6a8f7ef638af4d71a1", "IPY_MODEL_dad99ea4a9794840922ef79cf0da9651"], "layout": "IPY_MODEL_6845f0d9d1864f73bed8437c1cfe3bc5"}}, "13d5c6af4295427d8bdcb9e59c11a68d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d616896c953a4306a9b5c74ad9b7ffbc", "style": "IPY_MODEL_572e0399c5fb4e768c29479b9ebd6fd6", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d616896c953a4306a9b5c74ad9b7ffbc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "572e0399c5fb4e768c29479b9ebd6fd6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d874ff100da24dcda2dc03e3652d7cd8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e6600a99263e43c8b461e20ba81c907b", "style": "IPY_MODEL_d97ade98dd9242c99fc36780707a8496", "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>"}}, "e6600a99263e43c8b461e20ba81c907b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d97ade98dd9242c99fc36780707a8496": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bc6e5dd410b34f6a8f7ef638af4d71a1": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "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": "e0f898fd-b705-43ed-85a5-d9c254e24e49"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["ants", "bees"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "bar", "uid": "cf2cb3a8-56bc-45cd-9da3-8d16dbb3736a"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": ["ants", "bees"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "bar", "uid": "96af5540-9c80-4767-8cae-33340ab5466d"}], "_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": "#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": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "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, "#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": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 4], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"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=="}]}, "dad99ea4a9794840922ef79cf0da9651": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_de92fb9690344de3bb8233b7253126ba", "style": "IPY_MODEL_ece5303ab45f47e285dc6d4b05bba0ec", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\" style=\"font-size: 14px;\">Go to top</a>"}}, "de92fb9690344de3bb8233b7253126ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ece5303ab45f47e285dc6d4b05bba0ec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6845f0d9d1864f73bed8437c1cfe3bc5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d49956b2eb948f69dc8a76b319b366f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1773157a5e7f471f9a8cc7b54ffe1885": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fa4e37dbaf224d0e8ee9317ebdc092f0", "style": "IPY_MODEL_1ad88d3cd52c4fddb295c3c95f305102", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "fa4e37dbaf224d0e8ee9317ebdc092f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1ad88d3cd52c4fddb295c3c95f305102": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c6d8e77d21e45738f47af951503baa4": {"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_1523def7203a48e1a505c0d526c6c8bd", "IPY_MODEL_c037337c419d4ee484e1180a17400e47", "IPY_MODEL_6c686d28ab7642e1a4904979172f2bc0", "IPY_MODEL_197a38dced9b40b9adc9b1e0ecaced2f"], "layout": "IPY_MODEL_74313b4d05ff4b26bf6033b19ad7ac86"}}, "1523def7203a48e1a505c0d526c6c8bd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c88497c4c89146c798af0c8c89d0a06e", "style": "IPY_MODEL_8d5db0eea6f84505a90cc376a899f6b3", "value": "<h4 id=\"ImagePropertyDrift_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\"><b>Image Property Drift</b></h4>"}}, "c88497c4c89146c798af0c8c89d0a06e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8d5db0eea6f84505a90cc376a899f6b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c037337c419d4ee484e1180a17400e47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2f768241e4ba4ed4bc90130912d580bf", "style": "IPY_MODEL_dfd18ce462bd49afb2a724aabdb9cbab", "value": "<p>    Calculate drift between train dataset and test dataset per image property, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "2f768241e4ba4ed4bc90130912d580bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dfd18ce462bd49afb2a724aabdb9cbab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6c686d28ab7642e1a4904979172f2bc0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_96c76db4266c4a7393eafaf37c359820", "style": "IPY_MODEL_7cd5b1b2caf14b368a8877ea0616ab3e", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_104b0_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104b0_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104b0_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104b0_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104b0_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_104b0_\">\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_104b0_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_104b0_row0_col1\" class=\"data row0 col1\" >Earth Mover's Distance < 0.1 for image properties drift</td>\n      <td id=\"T_104b0_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"}}, "96c76db4266c4a7393eafaf37c359820": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7cd5b1b2caf14b368a8877ea0616ab3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "197a38dced9b40b9adc9b1e0ecaced2f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_39db10959ba741c3b3d98fc98635b235", "IPY_MODEL_97d72ea6e6c44c80a5c3d673c7318b4c", "IPY_MODEL_49ecd9290dfa4abe88bcfc733add559c", "IPY_MODEL_db9bf599b30e4bfaa0540f01c257e5f2", "IPY_MODEL_18473c26b1f34b52bcd3e710210c23ce", "IPY_MODEL_a8c1d30576f547d499ef1555f6b289be", "IPY_MODEL_693881600d36497285128844b082b1e4", "IPY_MODEL_2c706b97912345a58bfed18ba4af8b0a", "IPY_MODEL_eddc4056970c42259dd9062ec20a90c7", "IPY_MODEL_8d0cca79b8cc4c63a3ef2173eb6281f1"], "layout": "IPY_MODEL_07e06117be354853ae70f0359da8aded"}}, "39db10959ba741c3b3d98fc98635b235": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d119602117ed43a7ae8209075258cc0d", "style": "IPY_MODEL_2da9c458632443f7afdd8a181ead75e7", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d119602117ed43a7ae8209075258cc0d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2da9c458632443f7afdd8a181ead75e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "97d72ea6e6c44c80a5c3d673c7318b4c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_adf7ab0ca75a4b1c9d637db30e3e3b40", "style": "IPY_MODEL_148a70ddd07146c5b51059588bd2416e", "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'].<br></span></div>"}}, "adf7ab0ca75a4b1c9d637db30e3e3b40": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "148a70ddd07146c5b51059588bd2416e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49ecd9290dfa4abe88bcfc733add559c": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.04239275019502641], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": 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"1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_25d55ab40cdc48478f023fb002091777", "style": "IPY_MODEL_cff7b71479bd4bea8d7698c3a5b58c86", "value": "<h4 id=\"FeatureLabelCorrelationChange_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\"><b>Feature Label Correlation Change</b></h4>"}}, "25d55ab40cdc48478f023fb002091777": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cff7b71479bd4bea8d7698c3a5b58c86": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cbb323622df140e4a7abe98929d7304a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7ccd0baddbec49d79fbeaa8a944e7586", "style": "IPY_MODEL_f1cffd25cdd842f0871c3c15738dc7ec", "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/0.8/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>"}}, "7ccd0baddbec49d79fbeaa8a944e7586": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f1cffd25cdd842f0871c3c15738dc7ec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9ef9bae83490434084b62a0d7fd504b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d33c367f04994e05aed7949e706e9eba", "style": "IPY_MODEL_bc47f68670434a1ab41d028496b4517c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_ccdaa_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ccdaa_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ccdaa_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ccdaa_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ccdaa_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ccdaa_\">\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_ccdaa_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_ccdaa_row0_col1\" class=\"data row0 col1\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_ccdaa_row0_col2\" class=\"data row0 col2\" >Found highest PPS 0.09 for property Brightness and class bees</td>\n    </tr>\n  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"#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": "Brightness: Predictive Power Score (PPS) Per Class"}, "xaxis": {"range": [-3, 4], "title": {"text": "Class"}, "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": "eK2eHQI3tj8QcGMZlAqjPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAAAACwI8IUZzuyPw=="}]}, "c1e6adf880764cd1b957802dc4688158": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_67679804b7184b50a48d2246361d9c68", "style": "IPY_MODEL_42d8d28a27da46bbac1547024bf286a3", "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/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "67679804b7184b50a48d2246361d9c68": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "42d8d28a27da46bbac1547024bf286a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c697128f42247ad91ef623524c224de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8187bdf93a8c45e79c23b28393af2313", "style": "IPY_MODEL_b729cef516784a54b485ad03b223af57", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "8187bdf93a8c45e79c23b28393af2313": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b729cef516784a54b485ad03b223af57": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b6853a4718594a0bad6926695c6224c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f69248ce6d914796b415970784cb4825", "style": "IPY_MODEL_5ec054f9684f45d0ab57a2f34000f444", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "f69248ce6d914796b415970784cb4825": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ec054f9684f45d0ab57a2f34000f444": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f844ac82dd304b8a86ede281a3e904d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dcd8a214dd3a42388144b83cda893139", "style": "IPY_MODEL_9cc46afc5ca44b3caaceb08fe1053df9", "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>"}}, "dcd8a214dd3a42388144b83cda893139": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9cc46afc5ca44b3caaceb08fe1053df9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "605179ec82a44d98a0b22b1256290f2c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_16ba4d3c61ea4fbda096197c6b8ca847", "style": "IPY_MODEL_7cf5e80ef2b5470797e66fd3e5b9144a", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "16ba4d3c61ea4fbda096197c6b8ca847": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7cf5e80ef2b5470797e66fd3e5b9144a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2a0dc191bbc74dc08610fcc53c79c826": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6315eef1caab447cabd5b1e2c1f2381d", "style": "IPY_MODEL_ef7e107611a848eabfc5f0c45bfddae6", "value": "<div>   An even more powerful indication of dataset bias, as an image property that was powerful in train</div>"}}, "6315eef1caab447cabd5b1e2c1f2381d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef7e107611a848eabfc5f0c45bfddae6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c1091aa35d604dc4af8ef8cf70fe9321": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5b580465e0184845a53e1e7bc1926d3e", "style": "IPY_MODEL_b782c21a11da467a86687811e971d0df", "value": "<div>   but not in test can be explained by bias in train that is not relevant to a new dataset.</div>"}}, "5b580465e0184845a53e1e7bc1926d3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b782c21a11da467a86687811e971d0df": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3f90f376e31b4b6db71eb80ddadd5a69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c6e7a581d01040beb91ea7c3b497f148", "style": "IPY_MODEL_c31b9312ec4d40d78bf5fb090325357d", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "c6e7a581d01040beb91ea7c3b497f148": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c31b9312ec4d40d78bf5fb090325357d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d1aaceaf13db4ae5be5eb92c8b0ed74b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_94e3e24777ff43af8b3983bea85dc1dc", "style": "IPY_MODEL_cc29ee59594a41ed9cc645ff30f88cb0", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "94e3e24777ff43af8b3983bea85dc1dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc29ee59594a41ed9cc645ff30f88cb0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "565380c008004189be08b644e5157e11": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e02b7bfaaa84209880103d06a9af528", "style": "IPY_MODEL_0a062633c7fb41ce9e5667195f637ea4", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\" style=\"font-size: 14px;\">Go to top</a>"}}, "1e02b7bfaaa84209880103d06a9af528": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a062633c7fb41ce9e5667195f637ea4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4db957e7667042fd84cf12f3a49623f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b112b214b1d54f76ab35d3dd825455a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc05ec0815f94ac48fd6fd93c7c557e6": {"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_2136e95626d0418b87bfbc6f21557148", "IPY_MODEL_ce2156f91eeb4271b2f93599d218454a", "IPY_MODEL_17e088fe6c4a4e76a1bfe5ad1d6c9ee5", "IPY_MODEL_61e65adf7b0e4e0ab8f44a0f749bcfec"], "layout": "IPY_MODEL_0cce904b94b84b1a870f254058f55c13"}}, "2136e95626d0418b87bfbc6f21557148": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_36aac95dfe364e3a8d66b0f4ff77b2fe", "style": "IPY_MODEL_7a994817ddb547e1854c5aad32b55bc8", "value": "<h4 id=\"TrainTestPredictionDrift_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\"><b>Train Test Prediction Drift</b></h4>"}}, "36aac95dfe364e3a8d66b0f4ff77b2fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a994817ddb547e1854c5aad32b55bc8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ce2156f91eeb4271b2f93599d218454a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4837afa3dad24399a4b83ae7c1eb01aa", "style": "IPY_MODEL_48066625efdf40d28a7a83012dcc3ab7", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "4837afa3dad24399a4b83ae7c1eb01aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "48066625efdf40d28a7a83012dcc3ab7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17e088fe6c4a4e76a1bfe5ad1d6c9ee5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1d7488dffdab497ca7299fe3c57646ac", "style": "IPY_MODEL_d43636c094594faa922958d9a70e63ff", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b3f89_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b3f89_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b3f89_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b3f89_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b3f89_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b3f89_\">\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_b3f89_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_b3f89_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_b3f89_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"}}, "1d7488dffdab497ca7299fe3c57646ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d43636c094594faa922958d9a70e63ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "61e65adf7b0e4e0ab8f44a0f749bcfec": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a3aeca710a794feb8bc296a097339dad", "IPY_MODEL_ecd1ae9b8ed34466abc74ba0baaec57f", "IPY_MODEL_df95d8f2359e4a098f1b4416f474f96c", "IPY_MODEL_f68ff645e58c405db98c65d86f4b038a"], "layout": "IPY_MODEL_ae47ea5746244450a04337e80d879e99"}}, "a3aeca710a794feb8bc296a097339dad": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e2f9da332c14fee98e41be0595b486c", "style": "IPY_MODEL_797f04d17da0414c896e71cc712066f7", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1e2f9da332c14fee98e41be0595b486c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "797f04d17da0414c896e71cc712066f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ecd1ae9b8ed34466abc74ba0baaec57f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_12bd516aad7c404c9b718af53796fc2d", "style": "IPY_MODEL_7a21133a2adb470e8af5d6fcdd41f906", "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>"}}, "12bd516aad7c404c9b718af53796fc2d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a21133a2adb470e8af5d6fcdd41f906": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "df95d8f2359e4a098f1b4416f474f96c": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "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": "be04062e-66eb-40e7-99ca-06ac96cc9295"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["ants", "bees"], "xaxis": "x2", "y": 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"style": "IPY_MODEL_54ba0822375c4013b7b3049af6e523aa", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-IENFN\" style=\"font-size: 14px;\">Go to top</a>"}}, "5012dfd57ec74a8986816388e4ba425a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54ba0822375c4013b7b3049af6e523aa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ae47ea5746244450a04337e80d879e99": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0cce904b94b84b1a870f254058f55c13": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "beaa70e722de4987ac42a1b0573bf976": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5cdbdfd7bf25493d9c8db37ce15ef882": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a99e159811a64fd39689c31a0e4dc21d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f43232676fd41a4a9f5d93c58e32288": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_814cb94acd0c4b3d8eb37f6e182ebac4", "IPY_MODEL_a40f048fb29d4f86adbf52bd3634e66d"], "layout": "IPY_MODEL_33d89d77ebbb4b50bfe229f4f6f4aa05"}}, "814cb94acd0c4b3d8eb37f6e182ebac4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dbba051b79f045b582f8821fdd616026", "style": "IPY_MODEL_5ccc7412d5f046a39c498b716332d59e", "value": "<span id=\"summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\"></span>"}}, "dbba051b79f045b582f8821fdd616026": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ccc7412d5f046a39c498b716332d59e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a40f048fb29d4f86adbf52bd3634e66d": {"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": "Other"}, "children": ["IPY_MODEL_5e8b8b1de4e54db99324a00a2bfc4ff2"], "layout": "IPY_MODEL_7f81b0cfc88b4468b76f735e264885ad", "selected_index": null}}, "5e8b8b1de4e54db99324a00a2bfc4ff2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2b2a95737dca4cde982e879d40165449", "IPY_MODEL_ec6441bccdf14ddaa2de4a4e1d445ae8", "IPY_MODEL_bf2fd0d2bc1e4a5ea2ae2be214dcf987", "IPY_MODEL_c368d8788d1a4e4997a6439884e9f092", "IPY_MODEL_1ed4fe7b1ba3460e9bd2d1cd28666ef8", "IPY_MODEL_c368d8788d1a4e4997a6439884e9f092", "IPY_MODEL_eb71f82e68f9408aadb3991bac4148c5", "IPY_MODEL_c368d8788d1a4e4997a6439884e9f092", "IPY_MODEL_0605bb3307bf41a4ac7758a5ea1c0e9d", "IPY_MODEL_c368d8788d1a4e4997a6439884e9f092", "IPY_MODEL_be75769f25e14d48bbfdd9d7118327d0"], "layout": "IPY_MODEL_601c952783ba4671b50e059b00decfe5"}}, "2b2a95737dca4cde982e879d40165449": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_559500eaa762414db28bd777f032ee91", "style": "IPY_MODEL_087bd8a1f2c1472d89e486891c7f6bdc", "value": "<style type=\"text/css\">\n#T_57ade_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57ade_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57ade_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57ade_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57ade_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_57ade_\">\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_57ade_row0_col0\" class=\"data row0 col0\" >Image Dataset Drift</td>\n      <td id=\"T_57ade_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/0.8/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_57ade_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers-TestDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\">Image Property Outliers - Test Dataset</a></td>\n      <td id=\"T_57ade_row1_col1\" class=\"data row1 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.8/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_57ade_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers-TrainDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\">Image Property Outliers - Train Dataset</a></td>\n      <td id=\"T_57ade_row2_col1\" class=\"data row2 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.8/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_57ade_row3_col0\" class=\"data row3 col0\" ><a href=\"#ConfusionMatrix-TestDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\">Confusion Matrix - Test Dataset</a></td>\n      <td id=\"T_57ade_row3_col1\" class=\"data row3 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.8/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_57ade_row4_col0\" class=\"data row4 col0\" ><a href=\"#ConfusionMatrix-TrainDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\">Confusion Matrix - Train Dataset</a></td>\n      <td id=\"T_57ade_row4_col1\" class=\"data row4 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.8/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_57ade_row5_col0\" class=\"data row5 col0\" ><a href=\"#HeatmapComparison_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\">Heatmap Comparison</a></td>\n      <td id=\"T_57ade_row5_col1\" class=\"data row5 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/0.8/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"}}, "559500eaa762414db28bd777f032ee91": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "087bd8a1f2c1472d89e486891c7f6bdc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec6441bccdf14ddaa2de4a4e1d445ae8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c0106685c2694a8bbfc58e9108d2f970", "style": "IPY_MODEL_6948f65df0824456be43bc5598da9a56", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c0106685c2694a8bbfc58e9108d2f970": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6948f65df0824456be43bc5598da9a56": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bf2fd0d2bc1e4a5ea2ae2be214dcf987": {"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_629fc473b7334460ac7ac1d9af1bc819", "IPY_MODEL_e3f1d0cd947a4233b99e2ec73572de57", "IPY_MODEL_e3bc35d5c0144501a7e8fa0f56cf09d2"], "layout": "IPY_MODEL_97cdfac98e5f4298b2b1444747c6c4e2"}}, "629fc473b7334460ac7ac1d9af1bc819": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6688cadef39f4ba3bb9cdd3fcf474e8f", "style": "IPY_MODEL_4bf4bd99f83247118cf311e6e0f6e581", "value": "<h4 id=\"ImagePropertyOutliers-TestDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\"><b>Image Property Outliers - Test Dataset</b></h4>"}}, "6688cadef39f4ba3bb9cdd3fcf474e8f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4bf4bd99f83247118cf311e6e0f6e581": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e3f1d0cd947a4233b99e2ec73572de57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53e145db44754872bf91a335b6011a1a", "style": "IPY_MODEL_80e7ff6b5d664243b3f2bd6c9e51653d", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "53e145db44754872bf91a335b6011a1a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80e7ff6b5d664243b3f2bd6c9e51653d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e3bc35d5c0144501a7e8fa0f56cf09d2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a6f89613019f4e45a95b5b9162b5e9cf", "IPY_MODEL_0a75a2f54c774bbf95c1774dbd04650f", "IPY_MODEL_683fe360196b49dcadc1b6c2b20a06c9", "IPY_MODEL_a43628bb615b4adbbe2d45a9ecc5e05f", "IPY_MODEL_ad48da5b47bd4d8ba5f2101c25d7a3e0"], "layout": "IPY_MODEL_17aab42d56fd48499c9588dd739dc75a"}}, "a6f89613019f4e45a95b5b9162b5e9cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_66bbbb3cd8604026b2f890aa922dd0f7", "style": "IPY_MODEL_9494bf03c77643fdaa05efbb3b1c7c1d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "66bbbb3cd8604026b2f890aa922dd0f7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9494bf03c77643fdaa05efbb3b1c7c1d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0a75a2f54c774bbf95c1774dbd04650f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6f482779856640b49b6f0ffd1edb6fa0", "style": "IPY_MODEL_8594bc2bbf684499817207e61094b850", "value": "<div>\n<style>\n    .XGWTWI-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .XGWTWI-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .XGWTWI-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    .XGWTWI-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=\"XGWTWI-container\">\n    <div class=\"XGWTWI-row\">\n        <h5 class=\"XGWTWI-item\">Brightness</h5>\n        <div class=\"XGWTWI-item\">0.19</div>\n    </div>\n    <div class=\"XGWTWI-row\">\n        <h5 class=\"XGWTWI-item\">Image</h5>\n        <div class=\"XGWTWI-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    .EJKRHY-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .EJKRHY-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .EJKRHY-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    .EJKRHY-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=\"EJKRHY-container\">\n    <div class=\"EJKRHY-row\">\n        <h5 class=\"EJKRHY-item\">Mean Red Relative Intensity</h5>\n        <div class=\"EJKRHY-item\">0.15</div><div class=\"EJKRHY-item\">0.21</div><div class=\"EJKRHY-item\">0.61</div><div class=\"EJKRHY-item\">0.63</div><div class=\"EJKRHY-item\">0.66</div><div class=\"EJKRHY-item\">0.67</div><div class=\"EJKRHY-item\">0.7</div>\n    </div>\n    <div class=\"EJKRHY-row\">\n        <h5 class=\"EJKRHY-item\">Image</h5>\n        <div class=\"EJKRHY-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"EJKRHY-item\"><img 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\"/></div><div 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\"/></div><div 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src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwAAAQUBAQAAAAAAAAAAAAAAAAECAwQFBgf/xAA1EAABBAEDBAAEBAYCAgMAAAABAAIDEQQFEiETMUFRBiJhcTKBkaEUFSNCUtEzsWLBJEPw/8QAGgEAAgMBAQAAAAAAAAAAAAAAAQIAAwQFBv/EAC0RAAICAQQBAwEHBQAAAAAAAAABAhEDBBIhMUETIlFxBTJCYYGRsSOhwfDx/9oADAMBAAIRAxEAPwDK0139OVv1BVl/YKlppPVe3221df8AhWJdHuV2I1IRylYg90fA3kRxoUo7SnumoNjpChPaaTE9o4UQGOva2z3SNLj2HdOYwyEE/hCc5zGjlyZIrbFYeK8JN2zKnHprB+yRkgB72FXdLeRkfV1D8kWxatglCaE4Ko0MVOASNCeASaCZCtgASaCcSGCh3Q4iMUO6hJ5TdCrkUguNkqVt13UYKcCoqIyUBObY8qMORvpOmhKLLXkBKXg91W6lpQ5HgRwJ/lQoS9CPBFAy9OP/AMmh5aVfd+BZuA7ZmR/Wx+oWm/hhVC6LF2MaUHkpl8p7uBaPgZEbjym3yg90DkpCwc0KQEDl3ACjulWypuzG/mjdCyJZsw7tsfZRRkyO+bkKu1WIe6F2BInLGB7QBXPtK8t3Ha3ubJ+qbIeUgUYdvkcnAJGhOUGbHBSbhG2/Ka0hrbKryS7imuivsc6SykBUNpwKWx0S7ku6lCXnwi/alkJuoSlDlG1OCNkokBTtyj3ABRulA7I2LROXmkKtvc5Cm4nBTx3bcmMnsHBbMvYrGgjdJJTO7eVsv5j3e2goJ9orT5K/lIXEoPATR2UZZAEXQU+DCJ8trHfh7lST4T2xOnaKYSS37Kp5YqW1keSKe1lJ76bflU3fM61ajjORkMhaeXmgo8mB2NkyQuNlhq025XXkXcm6IgFPFwVCO6mj7ojokdyU5oSVynojCjhKPZQP2UckldkQMJZb4Cgu0hNoCAB1oolDR7S2gQAE8Jlp12oSx26k10qcGWniEI0DcVi9zk5rfasdIekx0fpSiWNBAQmkG+UKBNPEx2N0dsxaA9rHcqCB/UxI3e20t6PEa7A6B4G0ArHkijgmmghBDInUOfosWkyqU5Iw4silJopOK1Tg79Nw2NFOe4ucSOwVbS8M5ucGEfI025dTkxNdCWRgcDb9kNZqNklGPYM+o2SjFGNomCOtPIDua35WlT6w1uLphjaL2tofdamm4oxcfaBybNKR0LJJA5zQQ3mj7XPnmbybmYp6i8u59I5TQdMkfIMuYFrGci/JWVqDxJnzPabBceV3OfFI7COPjAN3CifS52fTI8bEcyi+V3lbMWoTm5y88I2afOpycpfsYI7qWNMDSHUe44UrRQXRR0EOHdPA5TQErn7B9VBrFleGilVc4kokeSUwKCDgngABNCC5QI60hKTkpwaoQTkqWMduEylNELCgGiVgUrWFIwKwxtqxIrk6ItiXol3hXGQ2LJACftDBwn2lTy/BkTwlouuyFdy5AIz7Qq5JJl8G2rOmEe2Tjs5Zs+ll2WZmD/kNO/2tiJzXssd2oB6sDjE4BwNH6LzmPJKEtxwI5ZQdoq4mNDp2O5w2izy4qSUubJB2DDZe4lUPiWRkGjNt9UePqpfhzKfqWlRT5bBxbGj3XFppqUo+tL5I3x6j5Lr8hpxXyMPH4WqHJnMGPGwcyPKzPibUo9OxmtbwZXfKAO1crmodbyMrKMrn9j28AJ8WlnljvXQ+PHF0da/Pk3iFrD9TSsxQ9Br55vmceQCmYzerDHPVNcL5VoyRSfK8/YKlu+AzklxFfU4bKBdmSuIAJeTwoz3AXUahpcDpDyGPItc3kQ9LKMbXbq8rtYM0ckaXZ18OaOSPAEBrd3hVZZNxUmTL/YPCrWrkXNipwTQUo5TAFtT4whO8TA/MKaR/aVE0J4CAXG1RaxsCWfMbjtHfkuHYN9omghjkLGSl9Hg+Fck1A4Gl4+NC175p27nbRyAT2VXR2HJzX9QtLGEWB3+yrTk7l4OVPXuGTa/BCIXi+LpSQNJ7LWfPF1HAwjpg/qrYwMWdgmgNE+B5VnK+9wWQ+0oT4MqOOzQFlW42iM2R+qmkZ0201oaPZ7qtvAP4rKuTSLN7mTl1ebtQTOrk8BAdXNqnmzbjsb+aaUqQ2OFuivkS9R/HYIUdWhUPk2JVwjs4X7Y2yh4MbxZUGpYmX/KnuwZjHku5Bb/cPRVeXHyf5A5mK+7a7bfrmlqQTmXHgLRZdFuofZefknBpr5PPT/I5L4myJMjTdPDzsJrcCexW18LzBmjRNc4Hp2L98rJ+LsV0+mQajhRv6Tmne3b+Ed7PpTfBmmyZemumy8mQQPO1kYNGh5v0tmSMXpVzVMkpRrk5TWdam1PKb1AwtiLg3b2Ivutr4K06HLzpcmZjTGB8rD5Ki+IdExsTP6GjN6oodX576ZJoAnxaj0zU3aHkyY1tLd3LyOarwts6lp6wKgY05K2ehTtifE6Fp5A+UBJjYboxvkALgOFxmkfE7Tm5E2UA1pFtP28LqMXUhm4omjunHhcbLgyYuJImySVRfBU1XT8jLkMrJHNeOFgZOHkYkbnSMc5x7ldhBPRqQUm58bZoHhlWQrsOqlj48GrFqZwai1weeE2bSK8/S8gPfxw091T2kEgrrxnGXTOmpX0IBasQRB79pcGCrLiLoJjWqQBMPXBIOixtNY+R3t52tH5Cyf1CUzHYAI4WkDu2MG/1tR3SBya9+UW7E9GP4m3+pbzdRjw9XiPQMroscBoHAFjnj9vzRiNONjHIdYkkcNxdxzZ/QcpXQNlzpZaB2sa3d4NDj96/RXGwHJwXSE/gBDAB2NcJPWqMYnnJ4WpybFjnhkhdtdQB7FXtL3CnDhjjQC5mCSXrGP5TVUXck+x6WtiS5DJ2kujq/kY3yPBT6nIpKvgTDCnZcz/kndbSW97HNKk+SNpFLdjhJhORICb4YCf3VTOxMd9NkqKSvlePP3CTFvro6eLVwVKRjyTDwq/LnWfKlfE5kjmPFOaaISUGhW8vs60aS4GhoHdCa96FLQx1OkQzY2ntx8p7ZHMFB4/utW9MjDMfpP4kFtA9NvhU2QY0Yi09+RI6Vo3AF1FwHn6pNQ1HE0rbJkTnqOFNYDZJ8fYLzslKbaXk85JWqRd1DoN07IxsyRsUL2FoeeAL9rlNL1QafDi4ex+57AXWDZ4oBvtdBi6Th5eL1cqd2S6YA7i7ivA48BSnDbPr/wDFzsaIsOMR44r+48k/9BWwljUXB8ixmoNrs5vXmjStHix5Cx2XmzGeZwHK5KFjsvKJcTt813pa3xhnSZvxFJCG22GmN2ckir/9qvh4peRNiStunOIcK7DsutgTjiTfb5LMb3e39WU9RwjjOdsfujobT7Pld/oTo9O0DEfKAJJw0CuwJHC4zRcCTVdVjikP9BtOk5rgrstakwHY+NpWPKOoyRtAH8AHtZ9bLe44v1YqScuOmarsaUm20QnbXdInYb7UlxGTxYrGl4e4cX9E/IbO+PZGQ1x7u9Lj3zRNzujm9W1FsD3QMaN/n6Ln6sknuVqahpohlkeZd7rsn2s7su3powUPadzBGKjwAFItA5S1S0l4AWlsAI5PYJ7InE8qEuiZkrf4bYL3nv8AkruJOGwugHIa7l3tVGQ14VlkRxZCXUWvFlodZB90leNI5muSUVt+SSTChL3ZLuP/ABHm0+CCmmQH5pTsYRxx7VcubKHFriR34PA9p2JmteOjKNr2csPtK4c2zn7Wo2dY7Y3Fa0AbWNAH5LLbD1y+R/4WjgeyqGZrBxQ3HkdyRz9VvQ7Rp8EbxtkkG9w9BbsDTjbMWa06M46GxunySy5BM7qLC48V6XNyPokHuF02fuzJxEx1RsHcLH1bScnExIc2d7XGcneAK2mzX6hLkg4qzqfZmstvHN8+DKc7nlCjJQqTtNnVQRSuAGqQMnDHuY14PLWnsT9VnZ3wQ2fMa+DKc2EjkSOLiPsVoaxDPHlQZ+A1zpgQyWEGuq3/AGFG3UdUys1uG/DlxserfI88/YUuPDJkit+N18/8PPSW4NZ1huht0/Hh2iGM7ZGiiXNAqh/tc9rWuZWp5jG4T8iDGfVvDT3P2+y0JKOXPm6higYwB2CXuBfr6ra07VNHcyPHxmtL6Dmxtb2TxccSUtltef8AfgMobVwO0HTMHTo5Jo6nlld/yPaAefC4TNnMWZLHC5rojIenXAIsgL0WQvfA2TILcPHicHECrPofr4XGaPh4eq/Ek7YMZ/8ALYX2Nx8/U/U2aTaWb9+SbsEMmyXHbM7S8qXFYzMmjkaXOLYXAfK+u4/VdfpGhY+oY8GbkSvJcS8gcWSqPx3kQRDTtPhAZG0mTawAD0B+5UUuqTwYGlwxSdNv/wBleR2AT5XLLBZIcOV/tyTG5uFHT6jnMwpoceJwc49x6HhByHyM5caPpZ8U8QZkzlokcHBw8nstPBy8bJha6JnJHYjsua40qoscdkerMPUsUx4sk0riATTB7WExpcV0uu4eRkTAh3yN7N8BYj43wj5m1zS6uka2U3ydTTTuHfJG2IeUU0FNJcT3SDhbDWkyZlelM1QMcB4TxIEyYGiwE6SFkrCXHlxN1xX/AO7qv1q7BIZnFR8lGTAsn3kTtDYhtbQZ4WbqhdHEZ4RbmG1ZLye5oe1HjQT5rnSkhuORTQRe4eykk1FWUaiEYw2eWPa+PLjgyXUXODSfv4Vt2vGAGSc7nnih4+iwv4d2HnBrXEsB5b4o3SualE1sXVazc55AaPr7S45KE+7TOXlxPJjtKqOs0kifFZMRRk+ZwPhvgKprWpnOmOAz5mu+Xj/P+39+Fk4mpRaXppifJcpaXG7Jc70rWgM/qDPnqmVITdgHwPyuyt8pqad+DnY3LFNSXZjdNzZSx4LXNNEHwULR1Mslz3zxihL8/wCqFlPX45b4KXydJnwy5cLG42QYJmH8baJ7elLBI+LHaMn+pI0U5w4v6rD+FRm5Ec+dq8z2Okc1zW2Giq4NeLpamfmYsMzYC5vVLdwZfJC8/lxyjL01zXwcGNSpUVc3Dxdf0zJEE7TJG7Yx4umOFHmu/dVNGZp+kQtMUM8+UR8xYwuA45o9h2VXS9Thdl5WPh4LoyCXy8gNP3Wzg61hzYplaxzWN/ybQV8vUxxcKe0bZ35HZjmSvGXlyDowjqMjdQDXeyVz3wvg5002RqeI0Nhkc4iFzq38+PHHtNzsp3xS8YmMHR40UtktHcV3J9LrsLGdpuisjxix8scVRh3Yn6/RRt4MWx9y8fCEk2uUvoea67lZWVrD/wCOiMcsB6YYO45v8+66/RtGizsKLL1GGQykE7XGr9cLVbpmNNmjUMjGhdlkDc4DgGq4tV8/KyXatiRaeeoIn/1/8Giu1+0+TVepFQx8USLkk15ZXwsAOMhYyTHPUI6T+ePC0o4ZMfuyvqFpysbNGDtp48rD1jV5dKLIjE17n3RtZE5ZZVEthklle1F7KzIYIx1uDXlctqWW3Kl/pimDsrTNQZqLHdet5VCeJsclNNhb9NiUZe7s6OlwLG+eyEN4ShqeAEoAXQo32NpFJ4pBpGgWMS0loJaRolkGWx5x9rbAdw4j69lPgTPhwGRPBDmtoqV3i1EWCtooUVnnFz4Mfp7sjmyKSEuka7vZtWDTg1rxbQkF7QOEqfHipW+y5QVUyjlYrBL06PPJdXYe1czcsfwseBhfI0NAJrivKeA13yvFgilUyInOmaQ07C4btvkfVG3F8nI1OiabcFwSubtZCN24dMUUKadjba1hsNbV/uhWL3cnW0624op/BlxT5vxRrhhY58WK11loNbGj/sp2Li5epfEr5I5t0GIRGZR5A8Lfw8A6bpokaCyfKO+UjiuOybp8EolEGO7pwFxcQ0AAe1llP2OUKSql/k48dPJrc5cX/BbdjtEckGNG0OcLe7/axn4L82WCBjGswY27tjeLdfY/RdFq2TBpmE7cSCewAsuKot1rTYorkyGRF1WHCiFzccsiW6KuyzcpRtkuPJhYMoihiDeqdztg4H3WhJqUQAEVX2sjsubycnDnfGNKyN0khBdQ4Y3yfv4SY7psjIZI0boDe199yDSksF+6X9xljxzpk2sZmp5cUrcMu29QREx9x7/LlXYZBpWPFFDGT3tzjdu82pdGh/hXZLMlwLZ5NwPhvAFfsl1fGbj6c58bupUm7jlByjKsa6/kT2qe1og/mZbKJXuMbf8AxPAVDX8bIyXtymudKwN/QKtJFIcZzDy4Cytj4dzGS438PkfiZxRV0Y+l/Uj4NP3Fviujl4yWu8hWWuJ5Nlb2paJFJJ1cZzRfcLHlxzBKY3EEj0t2PNDJ12bcOaORcDWlOCA0JwApaEXNgEJaSEIgFCRFI7KEFs9rNIRaUFFUAEBKikQAEvZA4So0CwCEoQiA15cwZULg4AbQaH/Szf5jkafZjY1zXkbgR6Vgt2kjyquTGJGkEWs708FDbXBlhjhW2uB0Ej9Rgkz84N/pk7APFDlcHqGQ7Iy3Sv7HgD0Ft5U+TFBJjNkcIHOstHkq1onwwzMhGVnktiPLWg1YVeLbp7nJ/Qw6vG37EUfhbHdkzyxODmskicGvHHI9Lq5o/wCWaNishshrQ0Oq+SoNKyMefV58PCiDMfChppHlxNK5ibcv4Yaxzix2LbX9Qcgt+yyambnO2uOOPrYmJqCS7OfzdTkLGbXl22UNeL4P0XVNexnw4818rBf/ALXIs05s080LXEGKQPA9g8rrcWWL+WTCQjolpDz/AI+LSZ1FbVEvyW+X+Ry+BlPlzJHA/K43R9LUyMd2O5uVD+YCycdrWuBYaIPC14M4m452fL7V+XHJS3RXBs2yXQ3JzDJC18UhafItUtxdZcSSfKny2QgXFV2q4V+GMVG0asUUo8DxwltNHPdOV4wvhIhJShBeUllKkRIAKNyXhOaWgEHygRsaHJ8cgDrKZwjhMBpMeXclAcE3hApEFD75Qm+EIgo03dvqoJuGc+Ap6tV8uxCSO47JprgzR7OfynxjKZ1DUYeN578XyrfxB8TvZM7E017QxooyN5/RU8yElxCxp4enL2Wf0YTkpS8GTWqXg6H4ABOo5YJNGIX+qh1mdpaY7Ac7Lde08bRwnfBTzFrVm9j2FppUNXDv5jKyiAx7hR92q3G9Q3+SMuODUKOnhYGaiHN53RCz9ldwoT/CZ2HfMgc9v5hVcYAvjfR4xgSVJkZThhQaniCzEae32PIXOduVG18qjnI5CDR7q7BP4PI9KDOnxZ5+rixOj3G3gni/oo2OruupF2rZ0IO1yaAYCbZ+iUBV45SDR/VW2ubKOaDvftMiy6GgBODRSC0tNEJOUwOw2opHJ7IuioQaQbTTadyk3IDDTYSWU/dwguG2q5UCR2fSXcUu5FhQgAlOaeeU20AhQjRIChNBCE1i0a0ZtoKZktuI8IQr30YvxGVNBdupZWVjF7gAOShCzXVj5UpLkTS3uxM1j+ajcHEDyptVMeRnzzx/ge8uCEIUt27yUxgqLujalL14caZw6Vbf9J2mZ7cSabEnp2O9xB+iELPLFFyarssUU20zMe1olcGfhBNfZStotHtCFeujTFEgtSskLShCI5cjm3t2uPPglKBdgdx3HkIQmFkq6EujwkPIQhQKGpPKEIDCVyghCFCCviqMPvukLBs3AoQljygKTGUlrnshCYcUA2hCEaFbP//Z\"/></div>\n    </div>\n</div>\n\n<style>\n    .JEQBFN-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .JEQBFN-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .JEQBFN-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    .JEQBFN-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=\"JEQBFN-container\">\n    <div class=\"JEQBFN-row\">\n        <h5 class=\"JEQBFN-item\">Mean Green Relative Intensity</h5>\n        <div class=\"JEQBFN-item\">0.16</div><div class=\"JEQBFN-item\">0.21</div><div class=\"JEQBFN-item\">0.55</div><div class=\"JEQBFN-item\">0.55</div><div class=\"JEQBFN-item\">0.66</div><div class=\"JEQBFN-item\">0.72</div><div class=\"JEQBFN-item\">0.74</div>\n    </div>\n    <div class=\"JEQBFN-row\">\n        <h5 class=\"JEQBFN-item\">Image</h5>\n        <div class=\"JEQBFN-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>"}}, "6f482779856640b49b6f0ffd1edb6fa0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8594bc2bbf684499817207e61094b850": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "683fe360196b49dcadc1b6c2b20a06c9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_20748dc50cc345b692d14513ffa466ad", "style": "IPY_MODEL_eba3803c46f24af4bd319b62ff75e7d5", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "20748dc50cc345b692d14513ffa466ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eba3803c46f24af4bd319b62ff75e7d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a43628bb615b4adbbe2d45a9ecc5e05f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_84f3a25c55284234b03fbb106bcfe654", "style": "IPY_MODEL_e78a7976a1a14ce7b1475b6eb45203c0", "value": "<style type=\"text/css\">\n#T_7e26f_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7e26f_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7e26f_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7e26f_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7e26f_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7e26f_\">\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_7e26f_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_7e26f_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, RMS Contrast, Mean Blue Relative Intensity</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "84f3a25c55284234b03fbb106bcfe654": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e78a7976a1a14ce7b1475b6eb45203c0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad48da5b47bd4d8ba5f2101c25d7a3e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_edf4ffb29068491aa687cf668d39ac85", "style": "IPY_MODEL_f18547ef677144d39e08934c9abe1a69", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\" style=\"font-size: 14px;\">Go to top</a>"}}, "edf4ffb29068491aa687cf668d39ac85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f18547ef677144d39e08934c9abe1a69": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17aab42d56fd48499c9588dd739dc75a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "97cdfac98e5f4298b2b1444747c6c4e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c368d8788d1a4e4997a6439884e9f092": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c35ffc434642466194d911fb2e1566aa", "style": "IPY_MODEL_b9b25488e63346b0bd9b96990fbf883b", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c35ffc434642466194d911fb2e1566aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b9b25488e63346b0bd9b96990fbf883b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1ed4fe7b1ba3460e9bd2d1cd28666ef8": {"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_4dda805c7efb43fe93fedc4444bc967d", "IPY_MODEL_59f8c83bc18b4e18a7f967ac578276d4", "IPY_MODEL_06d814e2b9d64d1b9805dbf970513929"], "layout": "IPY_MODEL_a74bf2eb80e14588807afce7e3374734"}}, "4dda805c7efb43fe93fedc4444bc967d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53734310bb1441f2b35d841f1fef13fc", "style": "IPY_MODEL_3c493e86cabd485a976861a9cf2c1690", "value": "<h4 id=\"ImagePropertyOutliers-TrainDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\"><b>Image Property Outliers - Train Dataset</b></h4>"}}, "53734310bb1441f2b35d841f1fef13fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c493e86cabd485a976861a9cf2c1690": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59f8c83bc18b4e18a7f967ac578276d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_656e4a89826441dc8eed45ab462e2bd2", "style": "IPY_MODEL_727113361fdf439cbce7e1bc92b5fb0c", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "656e4a89826441dc8eed45ab462e2bd2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "727113361fdf439cbce7e1bc92b5fb0c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "06d814e2b9d64d1b9805dbf970513929": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7f7ebb691c3c43b2b2b928a8cde7007c", "IPY_MODEL_2dac2b8062ae488a8fcc6be1cf6b01f5", "IPY_MODEL_6c45ceecdc0e424fa6d713a62a40264c", "IPY_MODEL_0ebe5257dc564d93b3e94680b333893e", "IPY_MODEL_3bc09666f78348c6ac51757eb37968bb"], "layout": "IPY_MODEL_46f066f538594bc6a60cf85388a02c5d"}}, "7f7ebb691c3c43b2b2b928a8cde7007c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8dc188d0ee6440d1802072ddfe92e5e1", "style": "IPY_MODEL_c33f17e8541e492c9f6356be9565a303", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8dc188d0ee6440d1802072ddfe92e5e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c33f17e8541e492c9f6356be9565a303": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2dac2b8062ae488a8fcc6be1cf6b01f5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_49de744f192a4d2ab8805acebe0f858d", "style": "IPY_MODEL_bf26fabaa46447359eb5e167fdee8d36", "value": "<div>\n<style>\n    .BKLSNZ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .BKLSNZ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .BKLSNZ-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    .BKLSNZ-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=\"BKLSNZ-container\">\n    <div class=\"BKLSNZ-row\">\n        <h5 class=\"BKLSNZ-item\">Brightness</h5>\n        <div class=\"BKLSNZ-item\">0.76</div><div class=\"BKLSNZ-item\">0.78</div><div class=\"BKLSNZ-item\">0.79</div><div class=\"BKLSNZ-item\">0.82</div><div class=\"BKLSNZ-item\">0.91</div>\n    </div>\n    <div class=\"BKLSNZ-row\">\n        <h5 class=\"BKLSNZ-item\">Image</h5>\n        <div class=\"BKLSNZ-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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IiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg/9k=\"/></div>\n    </div>\n</div>\n\n<style>\n    .TKYKND-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .TKYKND-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .TKYKND-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    .TKYKND-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=\"TKYKND-container\">\n    <div class=\"TKYKND-row\">\n        <h5 class=\"TKYKND-item\">RMS Contrast</h5>\n        <div class=\"TKYKND-item\">0.07</div><div class=\"TKYKND-item\">0.35</div><div class=\"TKYKND-item\">0.37</div>\n    </div>\n    <div class=\"TKYKND-row\">\n        <h5 class=\"TKYKND-item\">Image</h5>\n        <div class=\"TKYKND-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"TKYKND-item\"><img 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BuFXLUxjywtyB2CpDJph6SFSsKVvYbLHdE55wAt0yhcRuFcZQgHkiOfFG93RF0wpGgckQ1noiLbmojlilLhFIx5Y7S7S4HSex7FHyxxuY2SRjXSHSwOcAXHngd0RRvx7wiljnjEkMjJGHOHMcCDjbmFWSAMk4CIicplxDXBwBaQQeRClEVZoiIgIiICofDE85dG0n3CIoKmsa0eUAfCYREEOYCMELBlstumeXyUkbnHqWoimSrOVnpb/AOP2zUHCjjBHssoW6kbyhaiJkPKrwgiaMNY0fZVBgAwAiK4ajAzjCq0hEUwRpRETDX//2Q==\"/></div>\n    </div>\n</div>\n\n<style>\n    .ABMRBB-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .ABMRBB-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .ABMRBB-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    .ABMRBB-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=\"ABMRBB-container\">\n    <div class=\"ABMRBB-row\">\n        <h5 class=\"ABMRBB-item\">Mean Red Relative Intensity</h5>\n        <div class=\"ABMRBB-item\">0.13</div><div class=\"ABMRBB-item\">0.16</div><div class=\"ABMRBB-item\">0.18</div><div class=\"ABMRBB-item\">0.2</div><div class=\"ABMRBB-item\">0.6</div><div class=\"ABMRBB-item\">0.63</div><div class=\"ABMRBB-item\">0.65</div><div class=\"ABMRBB-item\">0.72</div><div class=\"ABMRBB-item\">0.83</div>\n    </div>\n    <div class=\"ABMRBB-row\">\n        <h5 class=\"ABMRBB-item\">Image</h5>\n        <div class=\"ABMRBB-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"ABMRBB-item\"><img 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\"/></div><div 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\"/></div><div 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l26Cce4nUXNc5CVH3PMfh2RbFJpTYwKAAE9jnA9zMpqW/wAQWiv6EPM6iPTXp7DTZkuO/Wcyo+Qrbf8AdtOAf5V7maoSbsXlitL1+/6jtUyXW7Qfy04HzMB1T1b1QYDnAM0ajCKAvU9BMWsBCJgY55+8djiujP5eSUU5LtGVizFSemSZsvBs0wfAJEVp9OzWgHp0m8qiha/0nIz74jZzSaoxeNglKEnLVnGYdTH0WoF9RxiVYFw4XoG4MBFBrLYyVjXTRhjyxz0P1FwYCteh6mSUKhapAGGK7l+47f0kgxpaGZecnyas9yJYgAwgZwGejYUoyZlEyiijBzITBzCSDSLzJmDIJZdBgwxAURiiCwWXjMAr6o7GBBx3gpgJggS/iFjiUSFHMlksF1QoQ4BE4GrQpqGOMnsBOzY+6czWXqWNaqS/TOOk1YLTGJJLZjRLH3MCBtGTBSixK2vtHLHAzOjpNGFC2Nw3XEy6121OqFK52g4A+e5mlTt0uhco1Un2YrwDqUG7dt+rHvAA83VHPKb8/YCXYoTUWuBtRW2j7YimtUIVr+pzgt7D2mhL7HPyTW3L7/8AQs6tltOAADux/iN3l9HWWPqFrD+oik0/msQT6hn95bVvSxUjjcDDaj6M0XlSbltfv/ANlXoYjrgcQmpNNLVuQHIHH3jaVZtSW/T05i33arWFmGFzLTKlBXaW3olWRpfMU+qs5kg0BhqfLOdrkqR95IMnBP6g8cc84/0nVaZ7ENCDSSTjtHcaL3QS0kkqiUVmSSSWQvEsCSSCU2MURiiSSAxcmWZJJJQJTMFEzO5Y/EkkOCGQXsAniIAXzdwUZ95JI6I9Il+pSnAILMeiiY7brVVnWtEJ79SJJI+EUkvyLduzn6nTu6hl+nv/AOZmFDEVqo5JkkmqE3Rg8jBDly+5ppQbt2PV2JPeEazdaAOQxOP2kklSbVsOEVxS+4OpR1ttrJwteMfPEJaOa1HGeZJJLfEWoL5P7/7sUig+IBlOQpyR8SSSSTgpdioZZQuvbP/Z\"/></div>\n    </div>\n</div>\n\n<style>\n    .JRKWXU-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .JRKWXU-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .JRKWXU-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    .JRKWXU-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=\"JRKWXU-container\">\n    <div class=\"JRKWXU-row\">\n        <h5 class=\"JRKWXU-item\">Mean Green Relative Intensity</h5>\n        <div class=\"JRKWXU-item\">0.16</div><div class=\"JRKWXU-item\">0.17</div><div class=\"JRKWXU-item\">0.17</div><div class=\"JRKWXU-item\">0.17</div><div class=\"JRKWXU-item\">0.18</div><div class=\"JRKWXU-item\">0.55</div><div class=\"JRKWXU-item\">0.57</div><div class=\"JRKWXU-item\">0.57</div><div class=\"JRKWXU-item\">0.58</div><div class=\"JRKWXU-item\">0.59</div>\n    </div>\n    <div class=\"JRKWXU-row\">\n        <h5 class=\"JRKWXU-item\">Image</h5>\n        <div class=\"JRKWXU-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"JRKWXU-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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\"/></div><div 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\"/></div>\n    </div>\n</div>\n</div>"}}, "49de744f192a4d2ab8805acebe0f858d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bf26fabaa46447359eb5e167fdee8d36": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6c45ceecdc0e424fa6d713a62a40264c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b11a80b63ccf47c5b59e6f010dcb55d2", "style": "IPY_MODEL_575f4d9beebf45cdb982a5adaf506c6a", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "b11a80b63ccf47c5b59e6f010dcb55d2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "575f4d9beebf45cdb982a5adaf506c6a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ebe5257dc564d93b3e94680b333893e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9e6d2980ba0e493983efc78f11041191", "style": "IPY_MODEL_204ea06581ce45008b78c10115a6339a", "value": "<style type=\"text/css\">\n#T_d3d8c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d3d8c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d3d8c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d3d8c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d3d8c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d3d8c_\">\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_d3d8c_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_d3d8c_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area, Mean Blue Relative Intensity</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "9e6d2980ba0e493983efc78f11041191": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "204ea06581ce45008b78c10115a6339a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3bc09666f78348c6ac51757eb37968bb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fe6b43a4d39d458f96749b99a1ef9c28", "style": "IPY_MODEL_99cfcc26bcd740eb8bf86604287b428f", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\" style=\"font-size: 14px;\">Go to top</a>"}}, "fe6b43a4d39d458f96749b99a1ef9c28": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "99cfcc26bcd740eb8bf86604287b428f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "46f066f538594bc6a60cf85388a02c5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a74bf2eb80e14588807afce7e3374734": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eb71f82e68f9408aadb3991bac4148c5": {"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_3f0774a37993487e86802038f4ede16c", "IPY_MODEL_6a1073972a374dc789adec0b80a0cb46", "IPY_MODEL_abb6241b35e6418e8648cfdd25e57eef"], "layout": "IPY_MODEL_82c7300df32641ebbb879729fba670e3"}}, "3f0774a37993487e86802038f4ede16c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f76e946aa54a4f7fac56e4ac671dab9c", "style": "IPY_MODEL_99bb0f7de55342d0a58034a9b04d6085", "value": "<h4 id=\"ConfusionMatrix-TestDataset_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\"><b>Confusion Matrix - Test Dataset</b></h4>"}}, "f76e946aa54a4f7fac56e4ac671dab9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "99bb0f7de55342d0a58034a9b04d6085": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6a1073972a374dc789adec0b80a0cb46": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6ced04a6bf69457d850b4f894713bd9d", "style": "IPY_MODEL_c2d82933aa404a8a817c80ad96fc60ff", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.8/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>"}}, "6ced04a6bf69457d850b4f894713bd9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c2d82933aa404a8a817c80ad96fc60ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "abb6241b35e6418e8648cfdd25e57eef": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c79466f77071497a81f503bec4202af1", "IPY_MODEL_a09c598ac2814466baa78f278039cd05", "IPY_MODEL_8d8e89e902a240f496f53d4dbb492515", "IPY_MODEL_8d3d36ef0b984c5aaf9cc77ad752b272"], "layout": "IPY_MODEL_884cb13ddf1c4b4e9d670635f9c39585"}}, "c79466f77071497a81f503bec4202af1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c55ebb391a134a9e8d47349c6d164ff9", "style": "IPY_MODEL_a29c3e5048814d80aea3792cd4f7a805", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c55ebb391a134a9e8d47349c6d164ff9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a29c3e5048814d80aea3792cd4f7a805": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a09c598ac2814466baa78f278039cd05": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e9e2ddbdaebd40e6867b09daad786209", "style": "IPY_MODEL_481d11d8ffa24c2699fd8669dcd1b20b", "value": "<div>Showing 10 of 2 classes:</div>"}}, "e9e2ddbdaebd40e6867b09daad786209": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "481d11d8ffa24c2699fd8669dcd1b20b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8d8e89e902a240f496f53d4dbb492515": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"colorbar": {"title": {"text": "% out of<br>True Values"}}, "text": [[1, 69], [3, 80]], "texttemplate": "%{z}%<br>(%{text})", "x": ["ants", "bees"], "y": ["ants", "bees"], "z": [["1.43", "98.57"], ["3.61", "96.39"]], "type": "heatmap", "uid": "9948eeb8-c039-4357-b052-c951aacf3b7f"}], "_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"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, 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"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}}, "8d3d36ef0b984c5aaf9cc77ad752b272": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3513bfbcfa6a4f11ad99684d612d6810", "style": "IPY_MODEL_4bab3750247b44de981d77486072790e", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\" style=\"font-size: 14px;\">Go to top</a>"}}, "3513bfbcfa6a4f11ad99684d612d6810": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4bab3750247b44de981d77486072790e": {"model_name": "DescriptionStyleModel", 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"_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "95ebdadb5b1f4aba883726454eaf8814": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d9347aa6654c405ca67a2515565283cc", "style": "IPY_MODEL_d5e9072b7b21494a906d45768151b38e", "value": "<br><a href=\"#summary_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\" style=\"font-size: 14px;\">Go to top</a>"}}, "d9347aa6654c405ca67a2515565283cc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5e9072b7b21494a906d45768151b38e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2073ac1d70b04a6ba377c24be645a039": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "de8d26da7a5e4edf8b5f1bbc800641e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "be75769f25e14d48bbfdd9d7118327d0": {"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_374dec88efc54133b6e3f3c101e4e732", "IPY_MODEL_96539b1348ff4a48946a4cc2473b5045", "IPY_MODEL_45fac2f3ff8b4e50a0cf2e2a196868a7"], "layout": "IPY_MODEL_869aabddd0b841858244aa06fdb013a7"}}, "374dec88efc54133b6e3f3c101e4e732": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_63981b37b73543e3bbd2e7a616622fcc", "style": "IPY_MODEL_6d65949cc141442083e615fa001378a8", "value": "<h4 id=\"HeatmapComparison_5QPAHKZ2U2TNHEYR8LNOEMQGY-section-VUPRT\"><b>Heatmap Comparison</b></h4>"}}, "63981b37b73543e3bbd2e7a616622fcc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d65949cc141442083e615fa001378a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96539b1348ff4a48946a4cc2473b5045": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d76338878a344b25bfdc80c5cf7a5ecf", "style": "IPY_MODEL_57426150797b4693a6639d503db0ba9d", "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/0.8/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>"}}, "d76338878a344b25bfdc80c5cf7a5ecf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57426150797b4693a6639d503db0ba9d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "45fac2f3ff8b4e50a0cf2e2a196868a7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_177817dbba874d92b5d0e6c3dd540942", "IPY_MODEL_61c97339dcc94285ba55f2734c520374", "IPY_MODEL_1d4c4fde998e40768f8432cc716c7e99"], "layout": "IPY_MODEL_33fa9ae9ffa84f83a5f9b025215d19e3"}}, "177817dbba874d92b5d0e6c3dd540942": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d8ac7482a2f94cf49cf0295dd4feee12", "style": "IPY_MODEL_f501730157094400bccf634aee06ccc6", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d8ac7482a2f94cf49cf0295dd4feee12": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f501730157094400bccf634aee06ccc6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "61c97339dcc94285ba55f2734c520374": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hoverinfo": "skip", "xaxis": "x", "yaxis": "y", "z": [[113, 113, 114, 113, 112, 112, 112, 113, 113, 114, 114, 114, 114, 115, 116, 115, 115, 116, 116, 116, 116, 117, 118, 118, 119, 120, 120, 120, 121, 120, 120, 120, 121, 121, 121, 120, 120, 120, 120, 120, 119, 119, 119, 118, 119, 119, 119, 121, 122, 121, 121, 122, 124, 124, 124, 125, 126, 124, 124, 125, 124, 124, 123, 122, 121, 120, 120, 120, 119, 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.. _sphx_glr_download_user-guide_vision_auto_quickstarts_plot_classification_tutorial.py:

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