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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_vision_auto_tutorials_quickstarts_plot_classification_tutorial.py>`
        to download the full example code

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

.. _sphx_glr_vision_auto_tutorials_quickstarts_plot_classification_tutorial.py:


.. _vision__classification_tutorial:

==============================================
Image Classification 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
:ref:`examples section  <vision__checks_gallery>`.

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

.. code-block:: bash

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

    # or install using pip from your python environment

.. GENERATED FROM PYTHON SOURCE LINES 27-32

Defining the data and model
===========================
.. note::
  In this tutorial, we use the pytorch to create the dataset and model. To see how this can be done using tensorflow
  or other frameworks, please visit the :ref:`creating VisionData guide <vision__vision_data_class>`.

.. GENERATED FROM PYTHON SOURCE LINES 34-37

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 37-47

.. code-block:: default


    import os
    import urllib.request
    import zipfile

    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 48-56

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 56-106

.. code-block:: default


    import albumentations as A
    import numpy as np
    import PIL.Image
    import torch
    import torchvision
    from albumentations.pytorch import ToTensorV2
    from torch import nn
    from torch.utils.data import DataLoader

    class AntsBeesDataset(torchvision.datasets.ImageFolder):

        def __getitem__(self, index: int):
            """overrides __getitem__ to be compatible to albumentations"""
            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):
                return np.array(image).astype('uint8')
            elif isinstance(image, np.ndarray):
                return image
            else:
                raise RuntimeError("Only PIL.Image and CV2 loaders currently supported!")

    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

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








.. GENERATED FROM PYTHON SOURCE LINES 107-110

Visualize the dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Let's see how our data looks like.

.. GENERATED FROM PYTHON SOURCE LINES 110-116

.. code-block:: default


    print(f'Number of training images: {len(train_dataset)}')
    print(f'Number of validation images: {len(test_dataset)}')
    print(f'Example output of an image shape: {train_dataset[0][0].shape}')
    print(f'Example output of a label: {train_dataset[0][1]}')





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

 .. code-block:: none

    Number of training images: 244
    Number of validation images: 153
    Example output of an image shape: torch.Size([3, 224, 224])
    Example output of a label: 0




.. GENERATED FROM PYTHON SOURCE LINES 117-120

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 120-130

.. code-block:: default


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

    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]
     70%|██████▉   | 31.1M/44.7M [00:00<00:00, 326MB/s]
    100%|██████████| 44.7M/44.7M [00:00<00:00, 336MB/s]




.. GENERATED FROM PYTHON SOURCE LINES 131-151

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.

Implementing the VisionData class
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The checks in the package validate the model & data by calculating various quantities over the data, labels and
predictions. In order to do that, those must be in a pre-defined format, according to the task type.
In the following example we're using pytorch. To see an implementation of this in tensorflow, please refer to the
:ref:`vision__vision_data_class` guide.
For pytorch, we will use our DataLoader, but we'll create a new collate function for it, that transforms the batch to
the correct format. Then, we'll create a :class:`deepchecks.vision.vision_data.vision_data.VisionData` object, that will hold the data loader.

To learn more about the expected formats, please visit the
:ref:`vision__supported_tasks`.

First, we'll create the collate function that will be used by the DataLoader.
In pytorch, the collate function is used to transform the output batch to any custom format, and we'll use that
in order to transform the batch to the correct format for the checks.

.. GENERATED FROM PYTHON SOURCE LINES 151-177

.. code-block:: default


    from deepchecks.vision.vision_data import BatchOutputFormat

    def deepchecks_collate_fn(batch) -> BatchOutputFormat:
        """Return a batch of images, labels and predictions for a batch of data. The expected format is a dictionary with
        the following keys: 'images', 'labels' and 'predictions', each value is in the deepchecks format for the task.
        You can also use the BatchOutputFormat class to create the output.
        """
        # batch received as iterable of tuples of (image, label) and transformed to tuple of iterables of images and labels:
        batch = tuple(zip(*batch))

        # images:
        inp = torch.stack(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
        images = np.clip(inp, 0, 1) * 255

        #labels:
        labels = batch[1]

        #predictions:
        logits = model.to(device)(torch.stack(batch[0]).to(device))
        predictions = nn.Softmax(dim=1)(logits)
        return BatchOutputFormat(images=images, labels=labels, predictions=predictions)








.. GENERATED FROM PYTHON SOURCE LINES 178-180

We have a single label here, which is the tomato class
The label_map is a dictionary that maps the class id to the class name, for display purposes.

.. GENERATED FROM PYTHON SOURCE LINES 180-186

.. code-block:: default


    LABEL_MAP = {
        0: 'ants',
        1: 'bees'
      }








.. GENERATED FROM PYTHON SOURCE LINES 187-189

Now that we have our updated collate function, we can recreate the dataloader in the deepchecks format, and use it
to create a VisionData object:

.. GENERATED FROM PYTHON SOURCE LINES 189-197

.. code-block:: default


    from deepchecks.vision import VisionData

    train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, collate_fn=deepchecks_collate_fn)
    test_loader = DataLoader(test_dataset, batch_size=4, shuffle=True, collate_fn=deepchecks_collate_fn)

    training_data = VisionData(batch_loader=train_loader, task_type='classification', label_map=LABEL_MAP)
    test_data = VisionData(batch_loader=test_loader, task_type='classification', label_map=LABEL_MAP)







.. GENERATED FROM PYTHON SOURCE LINES 198-204

Making sure our data is in the correct format:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The VisionData object automatically validates your data format and will alert you if there is a problem.
However, you can also manually view your images and labels to make sure they are in the correct format by using
the ``head`` function to conveniently visualize your data:

.. GENERATED FROM PYTHON SOURCE LINES 204-207

.. code-block:: default


    training_data.head()





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

 .. code-block:: none


    <deepchecks.core.serialization.html_display.HtmlDisplayableResult object at 0x7f9145bd72b0>



.. GENERATED FROM PYTHON SOURCE LINES 208-216

And observe the output:

Running Deepchecks' suite on our data and model!
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Now that we have defined the task class, we can validate the train and test data with deepchecks' train test validation
suite.
This can be done with this simple few lines of code:

.. GENERATED FROM PYTHON SOURCE LINES 216-222

.. code-block:: default


    from deepchecks.vision.suites import train_test_validation

    suite = train_test_validation()
    result = suite.run(training_data, test_data,  max_samples = 5000)





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

 .. code-block:: none


    Processing Batches:Train:
    |     | 0/1 [Time: 00:00]
    Processing Batches:Train:
    |█████| 1/1 [Time: 00:07]
    Processing Batches:Train:
    |█████| 1/1 [Time: 00:07]

    Computing Single Dataset Checks Train:
    |     | 0/0 [Time: 00:00]

    Computing Single Dataset Checks Train:
    |     | 0/0 [Time: 00:00]


    Processing Batches:Test:
    |     | 0/1 [Time: 00:00]


    Processing Batches:Test:
    |█████| 1/1 [Time: 00:04]


    Processing Batches:Test:
    |█████| 1/1 [Time: 00:04]



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



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




    Computing Train Test Checks:
    |      | 0/6 [Time: 00:00]




    Computing Train Test Checks:
    |      | 0/6 [Time: 00:00, Check=New Labels]




    Computing Train Test Checks:
    |      | 0/6 [Time: 00:00, Check=Heatmap Comparison]




    Computing Train Test Checks:
    |      | 0/6 [Time: 00:00, Check=Label Drift]       




    Computing Train Test Checks:
    |      | 0/6 [Time: 00:00, Check=Image Property Drift]




    Computing Train Test Checks:
    |████  | 4/6 [Time: 00:00, Check=Image Property Drift]




    Computing Train Test Checks:
    |████  | 4/6 [Time: 00:00, Check=Image Dataset Drift] 




    Computing Train Test Checks:
    |████  | 4/6 [Time: 00:00, Check=Property Label Correlation Change]




    Computing Train Test Checks:
    |██████| 6/6 [Time: 00:00, Check=Property Label Correlation Change]




    Computing Train Test Checks:
    |██████| 6/6 [Time: 00:00, Check=Property Label Correlation Change]
                                                   

                                                                    


                                                      



                                                                   




                                                                                                    



.. GENERATED FROM PYTHON SOURCE LINES 223-228

We also have suites for:
:func:`data integrity <deepchecks.vision.suites.data_integrity>`
- validating a single dataset and
:func:`model evaluation <deepchecks.vision.suites.model_evaluation>` -
evaluating the model's performance.

.. GENERATED FROM PYTHON SOURCE LINES 230-233

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

.. GENERATED FROM PYTHON SOURCE LINES 233-239

.. code-block:: default


    result.save_as_html('output.html')

    # Or displayed in a new window in an IDE like Pycharm:
    # result.show_in_window()





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

 .. code-block:: none


    'output (2).html'



.. GENERATED FROM PYTHON SOURCE LINES 240-241

Or, if working inside a notebook, the output can be displayed directly by simply printing the result object:

.. GENERATED FROM PYTHON SOURCE LINES 241-244

.. code-block:: default


    result






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        ~ ----------------------------------------------------------------------------
        ~ Copyright (C) 2021-2023 Deepchecks (https://www.deepchecks.com)
        ~
        ~ This file is part of Deepchecks.
        ~ Deepchecks is distributed under the terms of the GNU Affero General
        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
        ~ along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.
        ~ ----------------------------------------------------------------------------
        ~
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col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1cb39_row0_col1\" class=\"data row0 col1\" ><a href=\"#LabelDrift_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\">Label Drift</a></td>\n      <td id=\"T_1cb39_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_1cb39_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_1cb39_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1cb39_row1_col1\" class=\"data row1 col1\" ><a href=\"#ImagePropertyDrift_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\">Image Property Drift</a></td>\n      <td id=\"T_1cb39_row1_col2\" class=\"data row1 col2\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_1cb39_row1_col3\" class=\"data row1 col3\" >Passed for 7 properties out of 7 properties.\nFound property \"Mean Red Relative Intensity\" has the highest numerical drift score: 0.14</td>\n    </tr>\n    <tr>\n      <td id=\"T_1cb39_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1cb39_row2_col1\" class=\"data row2 col1\" ><a href=\"#PropertyLabelCorrelationChange_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\">Property Label Correlation Change</a></td>\n      <td id=\"T_1cb39_row2_col2\" class=\"data row2 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_1cb39_row2_col3\" class=\"data row2 col3\" >Found highest PPS 0.16 for property Mean Blue Relative Intensity and class bees</td>\n    </tr>\n    <tr>\n      <td id=\"T_1cb39_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1cb39_row3_col1\" class=\"data row3 col1\" >New Labels</td>\n      <td id=\"T_1cb39_row3_col2\" class=\"data row3 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_1cb39_row3_col3\" class=\"data row3 col3\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "66de6584178c4403a4487494de326c69": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9e301d4b19514ec0867393407c99a510": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a92b6f41de441e4862542867d5daf60": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f6e35c6f516e4ff1a7b2837b57c63fca", "style": "IPY_MODEL_a8a0dc21f74d44cdbf43931735ccc0a3", "value": "<hr style=\"background-color: 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"model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_43af2e70dff645da9fa3d264e9cd6636", "style": "IPY_MODEL_041ea43254684ab38ceb650fa2173171", "value": "<h4 id=\"LabelDrift_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\"><b>Label Drift</b></h4>"}}, "43af2e70dff645da9fa3d264e9cd6636": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "041ea43254684ab38ceb650fa2173171": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f5e17bcebd464f6d93b33dc359a39ec4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_57cbd7aefb2a44a189738695cb5fc005", "style": "IPY_MODEL_eae642fd21784fc6bd95d07fbe1152a7", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "57cbd7aefb2a44a189738695cb5fc005": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eae642fd21784fc6bd95d07fbe1152a7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c9cd3359b21b4a759d84c71ac986bb92": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b090673dbd7e4d879613403edf13f4c4", "style": "IPY_MODEL_2c5f89ff44fe4df29b36fe205314c097", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_8a083_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a083_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a083_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a083_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a083_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8a083_\">\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_8a083_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8a083_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_8a083_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"}}, "b090673dbd7e4d879613403edf13f4c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c5f89ff44fe4df29b36fe205314c097": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "36a17cbe1db44a0b80ee909f84a15d91": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_29a5208c6fc74b3ab8b842f0f82a92d4", "IPY_MODEL_d33ae66061c34fb5a96a268b4945b107", "IPY_MODEL_f3341d77390e40f6bc17ae1ee7f2615a", "IPY_MODEL_f916b06d2057454e831dd13f37455bb7", "IPY_MODEL_ef26d22f83e845d482b4d636c56935be"], "layout": "IPY_MODEL_9359c72da5f84baf8fa334103259f926"}}, "29a5208c6fc74b3ab8b842f0f82a92d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bedac6d5326d4902adbf9cb9cb7642b8", "style": "IPY_MODEL_822e87c0ff8b430aab5a27d4439c16ac", "value": "<h5><b>Additional Outputs</b></h5>"}}, "bedac6d5326d4902adbf9cb9cb7642b8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "822e87c0ff8b430aab5a27d4439c16ac": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d33ae66061c34fb5a96a268b4945b107": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ebdbad4e50844c95b1c87914e4c99423", "style": "IPY_MODEL_a21cb59ce6e14eb6bec1e19fd4c7d881", "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>"}}, "ebdbad4e50844c95b1c87914e4c99423": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a21cb59ce6e14eb6bec1e19fd4c7d881": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f3341d77390e40f6bc17ae1ee7f2615a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e747bdbce9e4712b84cf5cc9b885554", "style": "IPY_MODEL_a57b90f56fee4bcfa0111489871fc6d0", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "1e747bdbce9e4712b84cf5cc9b885554": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": 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{"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "tHmf4pIh4D+XDME62rzfPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "nfJHnfJH3T+xBlyxBlzhPw=="}]}, "ef26d22f83e845d482b4d636c56935be": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a7652a94748846e49e4d4feef70e4551", "style": "IPY_MODEL_f7394eab4fcb4ad5be4e98a2d8df13b8", "value": "<br><a href=\"#summary_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\" style=\"font-size: 14px;\">Go to top</a>"}}, "a7652a94748846e49e4d4feef70e4551": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7394eab4fcb4ad5be4e98a2d8df13b8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9359c72da5f84baf8fa334103259f926": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b93589a6e07247e5b1f32f7fa311098f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f210eea02fce45129b0f99476e56258b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bc8943312e464a1aa5f49820d6b315af", "style": "IPY_MODEL_9498afbf9a1a485f9e157b309fde015c", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "bc8943312e464a1aa5f49820d6b315af": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9498afbf9a1a485f9e157b309fde015c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2132ba36301e474e92936d2e7d36a178": {"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_0178c9e6cd224dc9b944ab3d721e5581", "IPY_MODEL_3537336ebdf849448f00019a29992222", "IPY_MODEL_4208f1a7f32a4b42927d84118156f971", "IPY_MODEL_fceb294002c04f93876bd5cdc514181b"], "layout": "IPY_MODEL_89f2303c20854121a3e8cabe39fa5065"}}, "0178c9e6cd224dc9b944ab3d721e5581": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_845a44718f2040ecb22eaa80440cf644", "style": "IPY_MODEL_718c8f85faed4a51aee2585ed7b7b061", "value": "<h4 id=\"ImagePropertyDrift_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\"><b>Image Property Drift</b></h4>"}}, "845a44718f2040ecb22eaa80440cf644": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "718c8f85faed4a51aee2585ed7b7b061": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3537336ebdf849448f00019a29992222": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b49465a58164263b74a58480c41cbec", "style": "IPY_MODEL_ba3dfd2c2da047398b99afe7602aeafe", "value": "<p>    Calculate drift between train dataset and test dataset per image property, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/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>"}}, "2b49465a58164263b74a58480c41cbec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ba3dfd2c2da047398b99afe7602aeafe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4208f1a7f32a4b42927d84118156f971": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c065f21fc16d4274bdaf558e1189dd0b", "style": "IPY_MODEL_219359bf70524601b16f650acb577368", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_07de2_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_07de2_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_07de2_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_07de2_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_07de2_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_07de2_\">\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_07de2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_07de2_row0_col1\" class=\"data row0 col1\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_07de2_row0_col2\" class=\"data row0 col2\" >Passed for 7 properties out of 7 properties.\nFound property \"Mean Red Relative Intensity\" has the highest numerical drift score: 0.14</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c065f21fc16d4274bdaf558e1189dd0b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "219359bf70524601b16f650acb577368": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fceb294002c04f93876bd5cdc514181b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8940a28ec82246679a805895cab9aae1", "IPY_MODEL_8142371a21bc433db28ca89054d95b65", "IPY_MODEL_06c42fb622384b23a1e9a9446e5b2ac8", "IPY_MODEL_0cdbdfcfdb6b42cbbb6252a67957f2c1", "IPY_MODEL_7ff044a82bbe42388096beeb492f2a4b", "IPY_MODEL_3f22a694213f41a99a9f091c4b22d4ea", "IPY_MODEL_c82f5ecf22df4fe78ccf46f407f5f802", "IPY_MODEL_7ef720b94c274daaa7ab6bae57e4675a", "IPY_MODEL_e90036c87f684da19bc42ed6d2a5e8a9", "IPY_MODEL_2e9a0c9e63df4131a17f58f4df8662ff", "IPY_MODEL_634b6e693c5848b5a1234a17c137aabe"], "layout": "IPY_MODEL_3f181ac258b5453aae69d9b4f52dd2fb"}}, "8940a28ec82246679a805895cab9aae1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9078dbd4c29443bcafa76e999b0845a6", "style": "IPY_MODEL_49890b61caa1420283b646dcd0e8bfd9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9078dbd4c29443bcafa76e999b0845a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "49890b61caa1420283b646dcd0e8bfd9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8142371a21bc433db28ca89054d95b65": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_efe5ab2461274c37a22fc59b55ecefe2", "style": "IPY_MODEL_b758ecd6efdd4a7d9b0ac2941e822f6f", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].</span></div>"}}, "efe5ab2461274c37a22fc59b55ecefe2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b758ecd6efdd4a7d9b0ac2941e822f6f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "06c42fb622384b23a1e9a9446e5b2ac8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7a00dc54090d42609ff373e9a6783127", "style": "IPY_MODEL_3c5d4960f1c0442598e6e049a89c9437", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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{"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1bfabf35aa2749b582659c32a255afe8", "style": "IPY_MODEL_38af573926b749b1b9e8605d97a99aec", "value": "<h4 id=\"PropertyLabelCorrelationChange_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\"><b>Property Label Correlation Change</b></h4>"}}, "1bfabf35aa2749b582659c32a255afe8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38af573926b749b1b9e8605d97a99aec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad224151d1154652847c8db962b67e12": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07be4f1fe9884c5bba19290724103ad4", "style": "IPY_MODEL_5c755af9d0aa4833b1b8a6e24db4d082", "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/0.18/vision/auto_checks/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>"}}, "07be4f1fe9884c5bba19290724103ad4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5c755af9d0aa4833b1b8a6e24db4d082": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e5db150da22741e5a1d958679afc77f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a60c852f89454fa494fbeaf41cb34ed8", "style": "IPY_MODEL_09f520a2a3c6401a865ffa370cf644a9", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_0d054_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0d054_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0d054_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0d054_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0d054_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0d054_\">\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_0d054_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_0d054_row0_col1\" class=\"data row0 col1\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_0d054_row0_col2\" class=\"data row0 col2\" >Found highest PPS 0.16 for property Mean Blue Relative Intensity and class bees</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a60c852f89454fa494fbeaf41cb34ed8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "09f520a2a3c6401a865ffa370cf644a9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e1990ab89d943709e4f7b34bafa5c02": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_658302f7f15948769041eb83b5bd7021", "IPY_MODEL_5930795e9d9d4aff8f2c195ae9e0e278", "IPY_MODEL_60424e371bb14e739419e477d1871395", "IPY_MODEL_8b168718a870401eb445c6a7e2d6e4e0", "IPY_MODEL_3466b571a7e8423d982b959d91d6ae49", "IPY_MODEL_7a86262c4ae644a7921aba14d8fafaaa", "IPY_MODEL_4ef5ef5280754aefa6f52376a847b1cb", 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(Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "0781091998ee4d368c649d41ee9f8651": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ea235df9ec5e4be2bbcb5c51e444d067": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3466b571a7e8423d982b959d91d6ae49": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7cbac3ed223c43a8967564144f03b666", "style": "IPY_MODEL_e72c2f881d434bddb0c0561702011192", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "7cbac3ed223c43a8967564144f03b666": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e72c2f881d434bddb0c0561702011192": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a86262c4ae644a7921aba14d8fafaaa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f49df9f51f74e1c9610ca0bc9784150", "style": "IPY_MODEL_3b08d5ca148743bdbb465eeb5dbc4b6d", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "4f49df9f51f74e1c9610ca0bc9784150": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3b08d5ca148743bdbb465eeb5dbc4b6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4ef5ef5280754aefa6f52376a847b1cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_99fcbd29063b421d9d566ac05d6b165f", "style": "IPY_MODEL_929ed382c5f94931a1bb9634508c11b9", "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>"}}, "99fcbd29063b421d9d566ac05d6b165f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "929ed382c5f94931a1bb9634508c11b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dc5d0ab12053429197573b61817296ff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4d9fe47dfb034af3ab11fd4bfaba167a", "style": "IPY_MODEL_86e002e835e24a72a6694cb53f673d50", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "4d9fe47dfb034af3ab11fd4bfaba167a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "86e002e835e24a72a6694cb53f673d50": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e9a8b0542c240c1a3cd821a4f090f69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e093477d52f3443ab87983f4ab07c256", "style": "IPY_MODEL_57cb84e809e94a33a9d24f4e6008089a", "value": "<div>   An even more powerful indication of dataset bias, as an image property that was powerful in train</div>"}}, "e093477d52f3443ab87983f4ab07c256": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57cb84e809e94a33a9d24f4e6008089a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "33ce952f1d0c431687534cea6769397f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e73de0a69163404f80737573ec4a9391", "style": "IPY_MODEL_e07b81e462184cd8afb3ac4a1f52c04a", "value": "<div>   but not in test can be explained by bias in train that is not relevant to a new dataset.</div>"}}, "e73de0a69163404f80737573ec4a9391": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e07b81e462184cd8afb3ac4a1f52c04a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "26b6baccdc4b422990a44dca4ed271e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a6a098300e947eabfffd1196e4d5a9e", "style": "IPY_MODEL_fc3166fc1ddd4bdab66f9baba35ebad0", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "8a6a098300e947eabfffd1196e4d5a9e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc3166fc1ddd4bdab66f9baba35ebad0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d9fa389dfee49c5bb1047bcaabba150": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a6e598d1c91d40508a3585b1f576e813", "style": "IPY_MODEL_e3aa2fce5e174927b51f6952e8231400", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "a6e598d1c91d40508a3585b1f576e813": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e3aa2fce5e174927b51f6952e8231400": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "66126ddbff73425aaa3a825c5b9fd0c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e61bc25b20e243c89434b052c10c7fb5", "style": "IPY_MODEL_8fdf16cf5a254059b5c6e3897326ca27", "value": "<br><a href=\"#summary_R4KZ36U8UEIHX74D0GJYV5G10-section-UR5L8\" style=\"font-size: 14px;\">Go to top</a>"}}, "e61bc25b20e243c89434b052c10c7fb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8fdf16cf5a254059b5c6e3897326ca27": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4fb51bf0d919436ba9687f4c36458c80": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5a63d1823da4e66af7d8733cc39ee5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6a61166aafd640ad8f25f00241719503": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7b17edfa5424db2a2530c2cd855670d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3170708686e41578419968209afe7aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "487af047cc4f497294679ab0dceb841d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_9dba078e13e74a1696b3ae050638b0a5", "IPY_MODEL_bc1e12baed0a4f4a8dc4ee9bda63150a"], "layout": "IPY_MODEL_efb4a298f19b429cb67e46a531ab352d"}}, "9dba078e13e74a1696b3ae050638b0a5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0d6d8bbdef3744369851bed8c156d8eb", "style": "IPY_MODEL_7bf0d8b5f4c64ebbb857c4c6487c74a8", "value": "<span id=\"summary_R4KZ36U8UEIHX74D0GJYV5G10-section-A78E9\"></span>"}}, "0d6d8bbdef3744369851bed8c156d8eb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7bf0d8b5f4c64ebbb857c4c6487c74a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bc1e12baed0a4f4a8dc4ee9bda63150a": {"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_852a095fa50840049b4cfe35495369ae"], "layout": "IPY_MODEL_e014b0d3f9914c85a09c1428c709a671", "selected_index": null}}, "852a095fa50840049b4cfe35495369ae": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7d7241e36258478da3266f8319a977d4", "IPY_MODEL_6e42ec1706344525a64ef8bba21befff", "IPY_MODEL_d87d2fa714814221b15d5fd4aa8f7e2b"], "layout": "IPY_MODEL_ee5738a671c14899ab56de88fe5719cd"}}, "7d7241e36258478da3266f8319a977d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd074912d3d74f72af6d47f484871917", "style": "IPY_MODEL_963dddbbf7b3412891b84eef9c0a5e08", "value": "<style type=\"text/css\">\n#T_c076d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c076d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c076d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c076d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c076d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c076d_\">\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_c076d_row0_col0\" class=\"data row0 col0\" ><a href=\"#HeatmapComparison_R4KZ36U8UEIHX74D0GJYV5G10-section-A78E9\">Heatmap Comparison</a></td>\n      <td id=\"T_c076d_row0_col1\" class=\"data row0 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/0.18/vision/auto_checks/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    <tr>\n      <td id=\"T_c076d_row1_col0\" class=\"data row1 col0\" >Image Dataset Drift</td>\n      <td id=\"T_c076d_row1_col1\" class=\"data row1 col1\" >Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/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  </tbody>\n</table>\n"}}, "fd074912d3d74f72af6d47f484871917": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "963dddbbf7b3412891b84eef9c0a5e08": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6e42ec1706344525a64ef8bba21befff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fdb284eb3ebb4d60bd2ed6fd1bbb1c32", "style": "IPY_MODEL_e5cd6127d3854cd7ad7453f6a490c81d", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "fdb284eb3ebb4d60bd2ed6fd1bbb1c32": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5cd6127d3854cd7ad7453f6a490c81d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d87d2fa714814221b15d5fd4aa8f7e2b": {"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_7e7c037d3f2c47e3af2e15c3e815a731", "IPY_MODEL_e563115d011449f2bff5393dedb65a35", "IPY_MODEL_40499b120a394efe8992f9559ccf560f"], "layout": "IPY_MODEL_08b331cb3acc43259fdfad39d1f48e97"}}, "7e7c037d3f2c47e3af2e15c3e815a731": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a5fb6971e5d4945ab1b53092ec58447", "style": "IPY_MODEL_aaf22bbb64da41aabe55af43a8197958", "value": "<h4 id=\"HeatmapComparison_R4KZ36U8UEIHX74D0GJYV5G10-section-A78E9\"><b>Heatmap Comparison</b></h4>"}}, "5a5fb6971e5d4945ab1b53092ec58447": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aaf22bbb64da41aabe55af43a8197958": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e563115d011449f2bff5393dedb65a35": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b82c94691a284a398ea2825e3b25ec46", "style": "IPY_MODEL_b49aafe3e5814362ac05c3b44506d938", "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/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b82c94691a284a398ea2825e3b25ec46": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b49aafe3e5814362ac05c3b44506d938": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "40499b120a394efe8992f9559ccf560f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d6026cc38b53406d912543a5d200fb25", "IPY_MODEL_e446b58674554cfba14498b71dc08b44", "IPY_MODEL_30129987d7d04a0e8a226160aef076ce"], "layout": "IPY_MODEL_71e7d44c1a2f4a87836133b2f9c762fc"}}, "d6026cc38b53406d912543a5d200fb25": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d1c85b7cb79a4a3b93ca92c4154433ee", "style": "IPY_MODEL_3fffa63d590b4d9db7785c9367932117", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d1c85b7cb79a4a3b93ca92c4154433ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3fffa63d590b4d9db7785c9367932117": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e446b58674554cfba14498b71dc08b44": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "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, 118, 119, 119, 119, 120, 120, 121, 122, 123, 123, 122, 123, 122, 123, 123, 122, 122, 123, 123, 124, 124, 123, 122, 123, 123, 123, 124, 125, 124, 124, 125, 126, 125, 125, 127, 127, 125, 124, 124, 123, 124, 124, 123, 124, 124, 124, 124, 123, 124, 123, 123, 122, 122, 121, 123, 123, 122, 122, 123, 123, 122, 122, 121, 120, 120, 121, 121, 121, 121, 120, 120, 120, 120, 121, 121, 121, 121, 120, 121, 121, 121, 122, 122, 122, 122, 122, 123, 124, 124, 125, 126, 126, 126, 126, 124, 124, 124, 124, 124, 125, 125, 125, 125, 125, 126, 126, 126, 126, 127, 126, 126, 126, 125, 125, 126, 126, 126, 125, 125, 125, 125, 125, 125, 124, 123, 123, 123, 123, 124, 123, 123, 122, 124, 124, 124, 124, 123, 123, 125, 125, 123, 125, 124, 124, 124, 124, 125, 126, 126, 126, 127, 127, 127, 127, 127], [114, 114, 114, 113, 112, 112, 113, 112, 112, 113, 114, 114, 115, 115, 115, 115, 115, 116, 115, 116, 116, 117, 117, 118, 119, 119, 119, 120, 120, 120, 120, 122, 122, 121, 121, 120, 121, 120, 120, 119, 120, 119, 120, 120, 120, 119, 120, 121, 122, 121, 122, 123, 124, 125, 124, 127, 127, 125, 125, 124, 124, 124, 123, 123, 121, 120, 121, 119, 119, 119, 119, 119, 120, 120, 121, 121, 123, 123, 123, 122, 122, 121, 122, 122, 122, 122, 122, 122, 124, 123, 123, 124, 123, 124, 124, 123, 124, 125, 124, 125, 125, 127, 126, 126, 126, 126, 125, 124, 125, 124, 125, 125, 125, 124, 124, 124, 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.. GENERATED FROM PYTHON SOURCE LINES 245-251

We can see that we do not have any meaningful issues with our data, and although there's some drift between the
train and test datasets (under the "Passed" section), this is not significant enough to cause any issues (and therefor
is not displayed in the "Didn't Pass" section).
However, under the "Other" section, that details checks without a specific pass/fail condition, we can see that the
heatmap of brightness in the images is not uniformly distributed, which means that in most images, there are brighter
objects in the center of the image. This makes sense as these images of bees and ants tend to have the insects in the
center of the image, but it is something to be aware of and maybe use data augmentation to fix.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 15.139 seconds)


.. _sphx_glr_download_vision_auto_tutorials_quickstarts_plot_classification_tutorial.py:

.. only:: html

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




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

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

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

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


.. only:: html

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