.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "vision/auto_tutorials/quickstarts/plot_detection_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_detection_tutorial.py>`
        to download the full example code

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

.. _sphx_glr_vision_auto_tutorials_quickstarts_plot_detection_tutorial.py:


.. _vision__detection_tutorial:

==========================
Object Detection Tutorial
==========================

In this tutorial, you will learn how to validate your **object detection 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>`.

If you just want to see the output of this tutorial, jump to the :ref:`observing the results <vision_segmentation_tutorial__observing_the_result>` section.

An object detection tasks usually consist of two parts:

- Object Localization, where the model predicts the location of an object in the image,
- Object Classification, where the model predicts the class of the detected object.

The common output of an object detection model is a list of bounding boxes around the objects, and
their classes.

.. 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 32-49

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:`vision__vision_data_class` guide.

Load Data
~~~~~~~~~
The model in this tutorial is used to detect tomatoes in images. The model is trained on a dataset consisted of
895 images of tomatoes, with bounding box annotations provided in PASCAL VOC format.
All annotations belong to a single class: tomato.

.. note::
    The dataset is available at the following link:
    https://www.kaggle.com/andrewmvd/tomato-detection

    We thank the authors of the dataset for providing the dataset.

.. GENERATED FROM PYTHON SOURCE LINES 49-125

.. code-block:: default


    import os
    import numpy as np
    import torch
    from torch.utils.data import DataLoader, Dataset

    import albumentations as A
    from albumentations.pytorch import ToTensorV2
    from PIL import Image
    import xml.etree.ElementTree as ET
    import urllib.request
    import zipfile

    url = 'https://figshare.com/ndownloader/files/34488599'
    urllib.request.urlretrieve(url, './tomato-detection.zip')

    with zipfile.ZipFile('./tomato-detection.zip', 'r') as zip_ref:
        zip_ref.extractall('.')

    class TomatoDataset(Dataset):
        def __init__(self, root, transforms):
            self.root = root
            self.transforms = transforms

            self.images = list(sorted(os.listdir(os.path.join(root, 'images'))))
            self.annotations = list(sorted(os.listdir(os.path.join(root, 'annotations'))))

        def __getitem__(self, idx):
            img_path = os.path.join(self.root, "images", self.images[idx])
            ann_path = os.path.join(self.root, "annotations", self.annotations[idx])
            img = Image.open(img_path).convert("RGB")
            bboxes, labels = [], []
            with open(ann_path, 'r') as f:
                root = ET.parse(f).getroot()

                for obj in root.iter('object'):
                    difficult = obj.find('difficult').text
                    if int(difficult) == 1:
                        continue
                    cls_id = 1
                    xmlbox = obj.find('bndbox')
                    b = [float(xmlbox.find('xmin').text), float(xmlbox.find('ymin').text), float(xmlbox.find('xmax').text),
                            float(xmlbox.find('ymax').text)]
                    bboxes.append(b)
                    labels.append(cls_id)

            bboxes = torch.as_tensor(np.array(bboxes), dtype=torch.float32)
            labels = torch.as_tensor(np.array(labels), dtype=torch.int64)

            if self.transforms is not None:
                res = self.transforms(image=np.array(img), bboxes=bboxes, class_labels=labels)

            target = {
                'boxes': [torch.Tensor(x) for x in res['bboxes']],
                'labels': res['class_labels']
            }
            img = res['image']

            return img, target

        def __len__(self):
            return len(self.images)

    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(),
    ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']))

    dataset = TomatoDataset(root='./tomato-detection/data', transforms=data_transforms)
    train_dataset, test_dataset = torch.utils.data.random_split(dataset,
                                                                [int(len(dataset)*0.9), len(dataset)-int(len(dataset)*0.9)],
                                                                generator=torch.Generator().manual_seed(42))
    test_dataset.transforms = A.Compose([ToTensorV2()])








.. GENERATED FROM PYTHON SOURCE LINES 126-129

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

.. GENERATED FROM PYTHON SOURCE LINES 129-136

.. code-block:: default


    print(f'Number of training images: {len(train_dataset)}')
    print(f'Number of test 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: 805
    Number of test images: 90
    Example output of an image shape: torch.Size([3, 224, 224])
    Example output of a label: {'boxes': [tensor([  0.00000,  75.13600,  39.68000, 165.75999]), tensor([ 0.00000,  0.00000, 94.08000, 93.56800])], 'labels': [tensor(1), tensor(1)]}




.. GENERATED FROM PYTHON SOURCE LINES 137-145

Downloading a Pre-trained Model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In this tutorial, we will download a pre-trained SSDlite model and a MobileNetV3 Large backbone
from the official PyTorch repository. For more details, please refer to the
`official documentation <https://pytorch.org/vision/stable/generated/torchvision.models.detection.ssdlite320_mobilenet_v3_large.html#torchvision.models.detection.ssdlite320_mobilenet_v3_large>`_.

After downloading the model, we will fine-tune it for our particular classes. We will do it by replacing the pre-trained
head with a new one that matches our needs.

.. GENERATED FROM PYTHON SOURCE LINES 145-162

.. code-block:: default

    from functools import partial
    from torch import nn
    import torchvision
    from torchvision.models.detection import _utils as det_utils
    from torchvision.models.detection.ssdlite import SSDLiteClassificationHead

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

    model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained=True)

    in_channels = det_utils.retrieve_out_channels(model.backbone, (320, 320))
    num_anchors = model.anchor_generator.num_anchors_per_location()
    norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.03)

    model.head.classification_head = SSDLiteClassificationHead(in_channels, num_anchors, 2, norm_layer)
    _ = model.to(device)





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

 .. code-block:: none

    Downloading: "https://download.pytorch.org/models/ssdlite320_mobilenet_v3_large_coco-a79551df.pth" to /home/runner/.cache/torch/hub/checkpoints/ssdlite320_mobilenet_v3_large_coco-a79551df.pth

      0%|          | 0.00/13.4M [00:00<?, ?B/s]
    100%|██████████| 13.4M/13.4M [00:00<00:00, 180MB/s]




.. GENERATED FROM PYTHON SOURCE LINES 163-166

Loading Pre-trained Weights
~~~~~~~~~~~~~~~~~~~~~~~~~~~
For this tutorial we will not include the training code itself, but will download and load pre-trained weights.

.. GENERATED FROM PYTHON SOURCE LINES 166-170

.. code-block:: default


    model.load_state_dict(torch.load('./tomato-detection/ssd_model.pth'))
    _ = model.eval()








.. GENERATED FROM PYTHON SOURCE LINES 171-189

Validating the Model With Deepchecks
=====================================
Now, after we have the training data, test 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
: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 format please visit
:ref:`vision__supported_tasks`.

First, we will create some functions that transform our batch to the correct format of images, labels and predictions:

.. GENERATED FROM PYTHON SOURCE LINES 189-256

.. code-block:: default


    def get_untransformed_images(original_images):
        """
        Convert a batch of data to images in the expected format. The expected format is an iterable of 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] in a uint8 format.
        """
        inp = torch.stack(list(original_images)).cpu().detach().numpy().transpose((0, 2, 3, 1))
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        # Un-normalize the images
        inp = std * inp + mean
        inp = np.clip(inp, 0, 1)
        return inp * 255

    def transform_labels_to_cxywh(original_labels):
        """
        Convert a batch of data to labels in the expected format. The expected format is an iterator of arrays, each array
        corresponding to a sample. Each array element is in a shape of [B, 5], where B is the number of bboxes
        in the image, and each bounding box is in the structure of [class_id, x, y, w, h].
        """
        label = []
        for annotation in original_labels:
            if len(annotation["boxes"]):
                bbox = torch.stack(annotation["boxes"])
                # Convert the Pascal VOC xyxy format to xywh format
                bbox[:, 2:] = bbox[:, 2:] - bbox[:, :2]
                # The label shape is [class_id, x, y, w, h]
                label.append(
                    torch.concat([torch.stack(annotation["labels"]).reshape((-1, 1)), bbox], dim=1)
                )
            else:
                # If it's an empty image, we need to add an empty label
                label.append(torch.tensor([]))
        return label

    def infer_on_images(original_images):
        """
        Returns the predictions for a batch of data. The expected format is an iterator of arrays, each array
        corresponding to a sample. Each array element is in a shape of [B, 6], where B is the number of bboxes in the
        predictions, and each bounding box is in the structure of [x, y, w, h, score, class_id].

        Note that model and device here are global variables, and are defined in the previous code block, as the collate
        function cannot recieve other arguments than the batch.
        """
        nm_thrs = 0.2
        score_thrs = 0.7
        imgs = list(img.to(device) for img in original_images)
        # Getting the predictions of the model on the batch
        with torch.no_grad():
            preds = model(imgs)
        processed_pred = []
        for pred in preds:
            # Performoing non-maximum suppression on the detections
            keep_boxes = torchvision.ops.nms(pred['boxes'], pred['scores'], nm_thrs)
            score_filter = pred['scores'][keep_boxes] > score_thrs

            # get the filtered result
            test_boxes = pred['boxes'][keep_boxes][score_filter].reshape((-1, 4))
            test_boxes[:, 2:] = test_boxes[:, 2:] - test_boxes[:, :2]  # xyxy to xywh
            test_labels = pred['labels'][keep_boxes][score_filter]
            test_scores = pred['scores'][keep_boxes][score_filter]

            processed_pred.append(
                torch.concat([test_boxes, test_scores.reshape((-1, 1)), test_labels.reshape((-1, 1))], dim=1))
        return processed_pred








.. GENERATED FROM PYTHON SOURCE LINES 257-260

Now 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 260-272

.. code-block:: default


    from deepchecks.vision.vision_data import BatchOutputFormat

    def deepchecks_collate_fn(batch) -> BatchOutputFormat:
        """Return a batch of images, labels and predictions in the deepchecks format."""
        # batch received as iterable of tuples of (image, label) and transformed to tuple of iterables of images and labels:
        batch = tuple(zip(*batch))
        images = get_untransformed_images(batch[0])
        labels = transform_labels_to_cxywh(batch[1])
        predictions = infer_on_images(batch[0])
        return BatchOutputFormat(images=images, labels=labels, predictions=predictions)








.. GENERATED FROM PYTHON SOURCE LINES 273-275

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 275-280

.. code-block:: default


    LABEL_MAP = {
        1: 'Tomato'
    }








.. GENERATED FROM PYTHON SOURCE LINES 281-283

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 283-292

.. code-block:: default


    from deepchecks.vision.vision_data import VisionData

    train_loader = DataLoader(train_dataset, batch_size=64, collate_fn=deepchecks_collate_fn)
    test_loader = DataLoader(test_dataset, batch_size=64, collate_fn=deepchecks_collate_fn)

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





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

 .. code-block:: none

    torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)




.. GENERATED FROM PYTHON SOURCE LINES 293-299

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 299-302

.. code-block:: default


    training_data.head()





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

 .. code-block:: none


    <deepchecks.core.serialization.html_display.HtmlDisplayableResult object at 0x7f90d5d6db80>



.. GENERATED FROM PYTHON SOURCE LINES 303-307

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

.. GENERATED FROM PYTHON SOURCE LINES 307-313

.. code-block:: default


    from deepchecks.vision.suites import model_evaluation

    suite = model_evaluation()
    result = suite.run(training_data, test_data)





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

 .. code-block:: none


    Processing Batches:Train:
    |     | 0/1 [Time: 00:00]
    Processing Batches:Train:
    |█████| 1/1 [Time: 00:30]
    Processing Batches:Train:
    |█████| 1/1 [Time: 00:30]

    Computing Single Dataset Checks Train:
    |     | 0/4 [Time: 00:00]

    Computing Single Dataset Checks Train:
    |█▎   | 1/4 [Time: 00:00, Check=Mean Average Precision Report]

    Computing Single Dataset Checks Train:
    |██▌  | 2/4 [Time: 00:00, Check=Mean Average Recall Report]   

    Computing Single Dataset Checks Train:
    |█████| 4/4 [Time: 00:00, Check=Weak Segments Performance] 

    Computing Single Dataset Checks Train:
    |█████| 4/4 [Time: 00:00, Check=Weak Segments Performance]


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


    Processing Batches:Test:
    |█████| 1/1 [Time: 00:03]


    Processing Batches:Test:
    |█████| 1/1 [Time: 00:03]



    Computing Single Dataset Checks Test:
    |     | 0/4 [Time: 00:00]



    Computing Single Dataset Checks Test:
    |█████| 4/4 [Time: 00:00, Check=Weak Segments Performance]



    Computing Single Dataset Checks Test:
    |█████| 4/4 [Time: 00:00, Check=Weak Segments Performance]




    Computing Train Test Checks:
    |     | 0/2 [Time: 00:00]




    Computing Train Test Checks:
    |     | 0/2 [Time: 00:00, Check=Class Performance]




    Computing Train Test Checks:
    |██▌  | 1/2 [Time: 00:00, Check=Class Performance]




    Computing Train Test Checks:
    |██▌  | 1/2 [Time: 00:00, Check=Prediction Drift] 




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




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

                                                                                                     


                                                      



                                                                                                    




                                                                                  



.. GENERATED FROM PYTHON SOURCE LINES 314-319

We also have suites for:
:func:`data integrity <deepchecks.vision.suites.data_integrity>`
- validating a single dataset and
:func:`train test validation <deepchecks.vision.suites.train_test_validation>` -
validating the dataset split

.. GENERATED FROM PYTHON SOURCE LINES 321-326

.. _observing_the_result:

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

.. GENERATED FROM PYTHON SOURCE LINES 326-329

.. code-block:: default


    result.save_as_html('output.html')





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

 .. code-block:: none


    'output (3).html'



.. GENERATED FROM PYTHON SOURCE LINES 330-331

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

.. GENERATED FROM PYTHON SOURCE LINES 331-334

.. code-block:: default


    result






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <!--
        ~ ----------------------------------------------------------------------------
        ~ 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/>.
        ~ ----------------------------------------------------------------------------
        ~
    -->
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <title>Model Evaluation Suite</title>
        <link rel="icon" type="image/x-icon" href="https://deepchecks.com/wp-content/themes/deepchecks/assets/favicons/favicon.ico">
        <style>
            body {
                font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
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                line-height: 1.5;
                color: #212529;
                text-align: left;
                max-width: 1200px;
                margin: auto;
                background-color: white;
            }

            div.nbinput.container div.prompt *,
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            div.nbinput.container div.input_area pre,
            div.nboutput.container div.output_area pre,
            div.nbinput.container div.input_area .highlight,
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                border: none;
                padding: 0;
                margin: 0;
                box-shadow: none;
            }

            div.nbinput.container > div[class*=highlight],
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                margin: 0;
            }

            div.nbinput.container div.prompt *,
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                background: unset;
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            div.nboutput.container div.output_area div.highlight {
                color: unset;  /* override Pygments text color */
            }

            /* avoid gaps between output lines */
            div.nboutput.container div[class*=highlight] pre {
                line-height: normal;
            }

            /* input/output containers */
            div.nbinput.container,
            div.nboutput.container {
                display: -webkit-flex;
                display: flex;
                align-items: flex-start;
                margin: 0;
                width: 100%;
            }
            @media (max-width: 540px) {
                div.nbinput.container,
                div.nboutput.container {
                    flex-direction: column;
                }
            }

            /* input container */
            div.nbinput.container {
                padding-top: 5px;
            }

            /* last container */
            div.nblast.container {
                padding-bottom: 5px;
            }

            /* input prompt */
            div.nbinput.container div.prompt pre {
                color: #307FC1;
            }

            /* output prompt */
            div.nboutput.container div.prompt pre {
                color: #BF5B3D;
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            /* all prompts */
            div.nbinput.container div.prompt,
            div.nboutput.container div.prompt {
                width: 4.5ex;
                padding-top: 5px;
                position: relative;
                user-select: none;
            }

            div.nbinput.container div.prompt > div,
            div.nboutput.container div.prompt > div {
                position: absolute;
                right: 0;
                margin-right: 0.3ex;
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            @media (max-width: 540px) {
                div.nbinput.container div.prompt,
                div.nboutput.container div.prompt {
                    width: unset;
                    text-align: left;
                    padding: 0.4em;
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                div.nboutput.container div.prompt.empty {
                    padding: 0;
                }

                div.nbinput.container div.prompt > div,
                div.nboutput.container div.prompt > div {
                    position: unset;
                }
            }

            /* disable scrollbars on prompts */
            div.nbinput.container div.prompt pre,
            div.nboutput.container div.prompt pre {
                overflow: hidden;
            }

            /* input/output area */
            div.nbinput.container div.input_area,
            div.nboutput.container div.output_area {
                -webkit-flex: 1;
                flex: 1;
                overflow: auto;
            }
            @media (max-width: 540px) {
                div.nbinput.container div.input_area,
                div.nboutput.container div.output_area {
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                }
            }

            /* input area */
            div.nbinput.container div.input_area {
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                border-radius: 2px;
                /*background: #f5f5f5;*/
            }

            /* override MathJax center alignment in output cells */
            div.nboutput.container div[class*=MathJax] {
                text-align: left !important;
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            /* override sphinx.ext.imgmath center alignment in output cells */
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            div.nboutput.container div.output_area.stderr {
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greater than 0.3</td>\n      <td id=\"T_45324_row2_col3\" class=\"data row2 col3\" >mAP score is: 0.03</td>\n    </tr>\n    <tr>\n      <td id=\"T_45324_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_45324_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\">Weak Segments Performance</a></td>\n      <td id=\"T_45324_row3_col2\" class=\"data row3 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_45324_row3_col3\" class=\"data row3 col3\" >Found a segment with mean iou of 0 in comparison to an average score of 0.181 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_45324_row4_col0\" class=\"data row4 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_45324_row4_col1\" class=\"data row4 col1\" ><a href=\"#WeakSegmentsPerformance_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\">Weak Segments Performance</a></td>\n      <td id=\"T_45324_row4_col2\" class=\"data row4 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_45324_row4_col3\" class=\"data row4 col3\" >Found a segment with mean iou of 0.131 in comparison to an average score of 0.201 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "403de94bbaa14cf3a645855b994530ef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cee22103ef45468ea7accf33f1fb0c4d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f52892669a454ae0ac4300865ec5ef2b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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"IPY_MODEL_c9a8cc5d154847e291ccd18194affdc5"}}, "863926ce835944be97b22b05f496bed9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f2c22675ef24807848c0b68e183c2e6", "style": "IPY_MODEL_b3926d4f7cb94dd796e3e93851b434d7", "value": "<h4 id=\"ClassPerformance_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\"><b>Class Performance</b></h4>"}}, "4f2c22675ef24807848c0b68e183c2e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3926d4f7cb94dd796e3e93851b434d7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0da951779650498c8ac4ff6039547b97": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_374081922f1e4248847bdac3e6109e5e", "style": "IPY_MODEL_2974ca20188d428a8d3475613b73a42b", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "374081922f1e4248847bdac3e6109e5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2974ca20188d428a8d3475613b73a42b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d0c93044b110449b9ee5cdad2c43b5ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a11d3c08a0c84ade842161fb43084a28", "style": "IPY_MODEL_048bd2d1c989462ab91e4bedb6fa105f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_932fd_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_932fd_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_932fd_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_932fd_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_932fd_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_932fd_\">\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_932fd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_932fd_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_932fd_row0_col2\" class=\"data row0 col2\" >2 scores failed. Found max degradation of 49.42% for metric Average Precision and class Tomato.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a11d3c08a0c84ade842161fb43084a28": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "048bd2d1c989462ab91e4bedb6fa105f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "80ac21df8383492b8c019397b4956f42": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_426808b4889143e4a20a9e4d18fca2e0", "IPY_MODEL_6cf9148a3157419d98f3ce3752dd12ab", "IPY_MODEL_e75ecfd3eeeb4cf9a467dfb37daf665f"], "layout": "IPY_MODEL_47ca1495474a45e399aca343f410d461"}}, "426808b4889143e4a20a9e4d18fca2e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], 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"yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true, "title": {"text": "Value"}}, "xaxis2": {"anchor": "y2", "domain": [0.525, 1.0], "matches": "x", "title": {"text": "Class"}, "type": "category"}, "yaxis2": {"anchor": "x2", "domain": [0.0, 1.0], "showticklabels": true, "title": {"text": "Value"}}}, "_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": "a7nPcI9Srz8="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "Nr3odJkgnT8="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "gaoocvZKpj8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "XTbPw752jT8="}]}, "e75ecfd3eeeb4cf9a467dfb37daf665f": {"model_name": "HTMLModel", "model_module": 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{"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6df8f960cbcc407691f902e4c2cdc31b", "style": "IPY_MODEL_20ea716aa75d45228f045f6c5102dda9", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "6df8f960cbcc407691f902e4c2cdc31b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "20ea716aa75d45228f045f6c5102dda9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed52849512bb4c9ab519d8dee84558c3": {"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_dc100b5f38d442c59151ee5035d93ec8", "IPY_MODEL_15c3aac657cf4cb0a59ceb0def3d41c3", "IPY_MODEL_93d257be5b9e4e1b98de4f635cd4a267", "IPY_MODEL_e1191ebd790549c395c6c1ecdf0bcd8b"], "layout": "IPY_MODEL_fedfacd5a352409cab0b3860393296cd"}}, "dc100b5f38d442c59151ee5035d93ec8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8ef554f25d734041b75016abd7e7d618", "style": "IPY_MODEL_2175e7c1eb9e404ea915c65079f70280", "value": "<h4 id=\"MeanAveragePrecisionReport_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\"><b>Mean Average Precision Report</b></h4>"}}, "8ef554f25d734041b75016abd7e7d618": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2175e7c1eb9e404ea915c65079f70280": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "15c3aac657cf4cb0a59ceb0def3d41c3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_156c744e64c84270acb5f2840c1f8508", "style": "IPY_MODEL_b812f0f462d845a2917231d354948700", "value": "<p>Summarize mean average precision metrics on a dataset and model per IoU and bounding box area. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_mean_average_precision_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "156c744e64c84270acb5f2840c1f8508": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b812f0f462d845a2917231d354948700": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93d257be5b9e4e1b98de4f635cd4a267": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f58ad1e25fe4c9eafdc7e4caff503e1", "style": "IPY_MODEL_9053cf1fb0e54bac8aa30feb745597bf", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_862f1_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_862f1_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_862f1_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_862f1_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_862f1_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_862f1_\">\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_862f1_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_862f1_row0_col1\" class=\"data row0 col1\" >mAP score is greater than 0.3</td>\n      <td id=\"T_862f1_row0_col2\" class=\"data row0 col2\" >mAP score is: 0.01</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3f58ad1e25fe4c9eafdc7e4caff503e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9053cf1fb0e54bac8aa30feb745597bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e1191ebd790549c395c6c1ecdf0bcd8b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3f702c6bbf014f648d887d6a25fd3afd", "IPY_MODEL_e43813638c924563810930abaeae4d82", "IPY_MODEL_e982850c8ca94463897a7e5a7df6ba71", "IPY_MODEL_76fba74fae754690bfe505257a2468fa"], "layout": "IPY_MODEL_1b715f59bdb84f78af5eb71300bbbf6f"}}, "3f702c6bbf014f648d887d6a25fd3afd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_24c72314929e45de87ab66aa3cdfd7e2", "style": "IPY_MODEL_a1048268a2054c2297ac5eecb05b94e7", "value": "<h5><b>Additional Outputs</b></h5>"}}, "24c72314929e45de87ab66aa3cdfd7e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1048268a2054c2297ac5eecb05b94e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e43813638c924563810930abaeae4d82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_309b53110dea4c0993822454b757ee7e", "style": "IPY_MODEL_d7f7e953250e4ef1a09bd53bcf739861", "value": "<style type=\"text/css\">\n#T_3b0bc_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3b0bc_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3b0bc_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3b0bc_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3b0bc_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3b0bc_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >mAP@[.50::.95] (avg.%)</th>\n      <th class=\"col_heading level0 col1\" >mAP@.50 (%)</th>\n      <th class=\"col_heading level0 col2\" >mAP@.75 (%)</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Area size</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_3b0bc_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n      <td id=\"T_3b0bc_row0_col0\" class=\"data row0 col0\" >0.01</td>\n      <td id=\"T_3b0bc_row0_col1\" class=\"data row0 col1\" >0.05</td>\n      <td id=\"T_3b0bc_row0_col2\" class=\"data row0 col2\" >0.01</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b0bc_level0_row1\" class=\"row_heading level0 row1\" >Small (area < 32^2)</th>\n      <td id=\"T_3b0bc_row1_col0\" class=\"data row1 col0\" >0.00</td>\n      <td id=\"T_3b0bc_row1_col1\" class=\"data row1 col1\" >0.01</td>\n      <td id=\"T_3b0bc_row1_col2\" class=\"data row1 col2\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b0bc_level0_row2\" class=\"row_heading level0 row2\" >Medium (32^2 < area < 96^2)</th>\n      <td id=\"T_3b0bc_row2_col0\" class=\"data row2 col0\" >0.06</td>\n      <td id=\"T_3b0bc_row2_col1\" class=\"data row2 col1\" >0.20</td>\n      <td id=\"T_3b0bc_row2_col2\" class=\"data row2 col2\" >0.02</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b0bc_level0_row3\" class=\"row_heading level0 row3\" >Large (area < 96^2)</th>\n      <td id=\"T_3b0bc_row3_col0\" class=\"data row3 col0\" >0.00</td>\n      <td id=\"T_3b0bc_row3_col1\" class=\"data row3 col1\" >0.00</td>\n      <td id=\"T_3b0bc_row3_col2\" class=\"data row3 col2\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "309b53110dea4c0993822454b757ee7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d7f7e953250e4ef1a09bd53bcf739861": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e982850c8ca94463897a7e5a7df6ba71": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"hovertemplate": "IoU threshold=%{x}<br>mAP (%)=%{y}<extra></extra>", "legendgroup": "", "line": {"color": "#636efa", "dash": "solid"}, "marker": {"symbol": "circle"}, "mode": "lines", "name": "", "orientation": "v", "showlegend": false, "x": {"dtype": "float64", "shape": 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thresholds"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "title": {"text": "IoU threshold"}}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "title": {"text": "mAP (%)"}}}, "_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, "x", "buffer"], "data": "AAAAAAAA4D+amZmZmZnhPzMzMzMzM+M/zczMzMzM5D9mZmZmZmbmPwAAAAAAAOg/mpmZmZmZ6T8zMzMzMzPrP8zMzMzMzOw/ZmZmZmZm7j8="}, {"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "BgvmbtEZqz+QrX77JeSgPw/SHG8RMZg/FzPxFFFKkD+d5TgXdvSFP90AvT0pBnc/cGmN5aPcWj+g9wM0ODY8PwAAAAAAAAAAAAAAAAAAAAA="}]}, "76fba74fae754690bfe505257a2468fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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"state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_802edc04379c4dcd82b25a25f6f48235", "IPY_MODEL_00924184cab248b7bd2fce47e1821ec0", "IPY_MODEL_5ce1d2d70fd440c69ffc32beba7761bf", "IPY_MODEL_27c9990c138c448b9ad9f5546bc7a118"], "layout": "IPY_MODEL_b344cdae85364199a9f232d611952743"}}, "802edc04379c4dcd82b25a25f6f48235": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_31be49feb7e14fac856b8bd4c65c749c", "style": "IPY_MODEL_59ba9693b57f4823923f1c88d3b29f40", "value": "<h4 id=\"MeanAveragePrecisionReport_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\"><b>Mean Average Precision Report</b></h4>"}}, "31be49feb7e14fac856b8bd4c65c749c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59ba9693b57f4823923f1c88d3b29f40": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "00924184cab248b7bd2fce47e1821ec0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_693dfaad39c549c2a08bfbacb2e04879", "style": "IPY_MODEL_6ee61cf323194df18d60ed4af66d8a6d", "value": "<p>Summarize mean average precision metrics on a dataset and model per IoU and bounding box area. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_mean_average_precision_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "693dfaad39c549c2a08bfbacb2e04879": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ee61cf323194df18d60ed4af66d8a6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5ce1d2d70fd440c69ffc32beba7761bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_48b0676e061041888fcbf9097c3393bf", "style": "IPY_MODEL_22cbcded278641cc861dfbcfac3c88d0", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b9f65_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9f65_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9f65_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9f65_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b9f65_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b9f65_\">\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_b9f65_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b9f65_row0_col1\" class=\"data row0 col1\" >mAP score is greater than 0.3</td>\n      <td id=\"T_b9f65_row0_col2\" class=\"data row0 col2\" >mAP score is: 0.03</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "48b0676e061041888fcbf9097c3393bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "22cbcded278641cc861dfbcfac3c88d0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "27c9990c138c448b9ad9f5546bc7a118": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4bd0ccc119eb4874b3f8845aab84b0e2", "IPY_MODEL_cf054273550943ec80c544549d964cb1", "IPY_MODEL_1623039177cb499fbbab446a63923fa0", "IPY_MODEL_589a923bc6444fa688ad80586dbd0f88"], "layout": "IPY_MODEL_318da3452ae74c0da8d299713c55d7da"}}, "4bd0ccc119eb4874b3f8845aab84b0e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2f4d0707c20944df9af6db4805f6e5b4", "style": "IPY_MODEL_d1f2e0573c014673b6c4a9993f48bc20", "value": "<h5><b>Additional Outputs</b></h5>"}}, "2f4d0707c20944df9af6db4805f6e5b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d1f2e0573c014673b6c4a9993f48bc20": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cf054273550943ec80c544549d964cb1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e0e16f6d36e42cdb3035237758f00be", "style": "IPY_MODEL_7358b6fef9da4048ac30f084feb5336d", "value": "<style type=\"text/css\">\n#T_7b20f_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b20f_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b20f_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b20f_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b20f_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7b20f_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >mAP@[.50::.95] (avg.%)</th>\n      <th class=\"col_heading level0 col1\" >mAP@.50 (%)</th>\n      <th class=\"col_heading level0 col2\" >mAP@.75 (%)</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Area size</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_7b20f_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n      <td id=\"T_7b20f_row0_col0\" class=\"data row0 col0\" >0.03</td>\n      <td id=\"T_7b20f_row0_col1\" class=\"data row0 col1\" >0.07</td>\n      <td id=\"T_7b20f_row0_col2\" class=\"data row0 col2\" >0.02</td>\n    </tr>\n    <tr>\n      <th id=\"T_7b20f_level0_row1\" class=\"row_heading level0 row1\" >Small (area < 32^2)</th>\n      <td id=\"T_7b20f_row1_col0\" class=\"data row1 col0\" >0.00</td>\n      <td id=\"T_7b20f_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_7b20f_row1_col2\" class=\"data row1 col2\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_7b20f_level0_row2\" class=\"row_heading level0 row2\" >Medium (32^2 < area < 96^2)</th>\n      <td id=\"T_7b20f_row2_col0\" class=\"data row2 col0\" >0.10</td>\n      <td id=\"T_7b20f_row2_col1\" class=\"data row2 col1\" >0.27</td>\n      <td id=\"T_7b20f_row2_col2\" class=\"data row2 col2\" >0.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_7b20f_level0_row3\" class=\"row_heading level0 row3\" >Large (area < 96^2)</th>\n      <td id=\"T_7b20f_row3_col0\" class=\"data row3 col0\" >0.02</td>\n      <td id=\"T_7b20f_row3_col1\" class=\"data row3 col1\" >0.04</td>\n      <td id=\"T_7b20f_row3_col2\" class=\"data row3 col2\" >0.02</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1e0e16f6d36e42cdb3035237758f00be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7358b6fef9da4048ac30f084feb5336d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1623039177cb499fbbab446a63923fa0": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"hovertemplate": "IoU threshold=%{x}<br>mAP (%)=%{y}<extra></extra>", 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true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Mean Average Precision over increasing IoU thresholds"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "title": {"text": "IoU threshold"}}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "title": {"text": "mAP (%)"}}}, "_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, "x", "buffer"], "data": "AAAAAAAA4D+amZmZmZnhPzMzMzMzM+M/zczMzMzM5D9mZmZmZmbmPwAAAAAAAOg/mpmZmZmZ6T8zMzMzMzPrP8zMzMzMzOw/ZmZmZmZm7j8="}, {"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "BSFOmC5zsT8514rrNLyqP2iHz+I3DqU/v5hhfP1PoD+QNvwyd9uZP3+pzGst+ZM//fA+IwNojj+OtiyKbl+HP/GgbeQr0YQ/+tliZfhGhD8="}]}, "589a923bc6444fa688ad80586dbd0f88": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_327b1955723049c585697ae3c99217d3", "style": "IPY_MODEL_ee91ce862a2b4fc5b75708cd6e84ef2b", "value": "<br><a href=\"#summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\" style=\"font-size: 14px;\">Go to top</a>"}}, "327b1955723049c585697ae3c99217d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee91ce862a2b4fc5b75708cd6e84ef2b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "318da3452ae74c0da8d299713c55d7da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b344cdae85364199a9f232d611952743": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "573a50c939334874bcf2a722c89c05a2": {"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_9211235cf21c4e7e84665954b9b43baf", "IPY_MODEL_3624c9571a4b49ebbf5a66aa50792e47", "IPY_MODEL_6693c6839d1a4cadb70c595643d07a05", "IPY_MODEL_9e41ad08039c48879fe1faed704b0122"], "layout": "IPY_MODEL_133f71bbd9234bcd825a5895f6b286e2"}}, "9211235cf21c4e7e84665954b9b43baf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_544540d6354144e8ab092d12432c827e", "style": "IPY_MODEL_ef97ca4eb5524bcbb46c8669b7ad52db", "value": "<h4 id=\"WeakSegmentsPerformance_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\"><b>Weak Segments Performance</b></h4>"}}, "544540d6354144e8ab092d12432c827e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef97ca4eb5524bcbb46c8669b7ad52db": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3624c9571a4b49ebbf5a66aa50792e47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_358d5b3744744021b83fbab263b81541", "style": "IPY_MODEL_a98eef69e30348b5bc8717551f940325", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "358d5b3744744021b83fbab263b81541": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a98eef69e30348b5bc8717551f940325": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6693c6839d1a4cadb70c595643d07a05": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6ad358a18fcb4a1d97ba58ad21f1e68a", "style": "IPY_MODEL_fc08cd8ad56a417baa1941279fc822dc", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_04a16_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04a16_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04a16_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04a16_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04a16_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_04a16_\">\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_04a16_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_04a16_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_04a16_row0_col2\" class=\"data row0 col2\" >Found a segment with mean iou of 0 in comparison to an average score of 0.181 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6ad358a18fcb4a1d97ba58ad21f1e68a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc08cd8ad56a417baa1941279fc822dc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", 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"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": "Mean IoU (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "Mean Red Relative Intensity"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "19b01596aefa4cff9e3e659576f9def2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ccfa80c1419f42708c79c9217cc81ff6", "style": "IPY_MODEL_701670fbd36b43238bfd49bd761b907d", "value": "<div>Check ran on 90 data samples. Mean IoU on the full data set is 0.18.</div>"}}, "ccfa80c1419f42708c79c9217cc81ff6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "701670fbd36b43238bfd49bd761b907d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ffc4d52c91c14375992108c5e9f038b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7844ec935c7644bab68ed9f8437d28a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3f6b7ceefb2342bab9bc4d5902081dfe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9d29b5205b246b385d1b78772e05abb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd95c3b5eac644b8b98a4c2f414f2f87", "style": "IPY_MODEL_4c85c5a6c6ff4ea6879598f2e0c47f1f", "value": "<br><a href=\"#summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\" style=\"font-size: 14px;\">Go to top</a>"}}, "fd95c3b5eac644b8b98a4c2f414f2f87": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c85c5a6c6ff4ea6879598f2e0c47f1f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "805fae0e143e438dba5b5334f3eaa368": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "133f71bbd9234bcd825a5895f6b286e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eea5700ce85846798ed48cbf2d6945ea": {"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_bc482e305e5d47c8b07e76cae56bb01a", "IPY_MODEL_15c06c0465214216b875ba761c98b01b", "IPY_MODEL_2daf2f56deab49dea0a8de23a182b42a", "IPY_MODEL_9909f0f4ee2346c48a3a0c657b32b91a"], "layout": "IPY_MODEL_6b6b32cbbfa7401db8e9e994ad6f0829"}}, "bc482e305e5d47c8b07e76cae56bb01a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c11ccd85a75a429d830b8085bb6b2029", "style": "IPY_MODEL_f2a255331cc7489a96ae94b54a08b72f", "value": "<h4 id=\"WeakSegmentsPerformance_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\"><b>Weak Segments Performance</b></h4>"}}, "c11ccd85a75a429d830b8085bb6b2029": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2a255331cc7489a96ae94b54a08b72f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "15c06c0465214216b875ba761c98b01b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e42a477bfda44de8d200e37e1983b0d", "style": "IPY_MODEL_f608623b726f47d2bb81c79900cf3484", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3e42a477bfda44de8d200e37e1983b0d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f608623b726f47d2bb81c79900cf3484": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2daf2f56deab49dea0a8de23a182b42a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4d12c77ba2f246488565081a5f6a9b41", "style": "IPY_MODEL_3dcad7d6da9a428b8fd35ccb0c196688", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_12bca_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_12bca_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_12bca_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_12bca_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_12bca_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_12bca_\">\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_12bca_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_12bca_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_12bca_row0_col2\" class=\"data row0 col2\" >Found a segment with mean iou of 0.131 in comparison to an average score of 0.201 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4d12c77ba2f246488565081a5f6a9b41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3dcad7d6da9a428b8fd35ccb0c196688": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9909f0f4ee2346c48a3a0c657b32b91a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1cb54c5ba37448f8a3865454289989af", "IPY_MODEL_ac1ad503d7c546e9b5e1282310d41e82", "IPY_MODEL_2490de82964b464ba532f08414025414", "IPY_MODEL_89656046f3a94b2d9f3ea11aae232ae4"], "layout": "IPY_MODEL_9134ea8652cd4afaaebcbc4c3b88742e"}}, "1cb54c5ba37448f8a3865454289989af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_598d3f8d48bc45898a1618b02f532ba2", "style": "IPY_MODEL_496b197bf05a4fc087849228b76e7922", "value": "<h5><b>Additional Outputs</b></h5>"}}, "598d3f8d48bc45898a1618b02f532ba2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "496b197bf05a4fc087849228b76e7922": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ac1ad503d7c546e9b5e1282310d41e82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6ab5629219564c6585c14b3b65627a86", "style": "IPY_MODEL_b0aa64fb730048d0bbe4699b3b03fb00", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "6ab5629219564c6585c14b3b65627a86": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b0aa64fb730048d0bbe4699b3b03fb00": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2490de82964b464ba532f08414025414": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c90707b733554582b42912ddc7ee13ff", "IPY_MODEL_0a13260b79614b1a924b7b7483cf3040"], "layout": 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"#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": "Mean IoU (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "Mean Red Relative Intensity"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "f8ece1f98e6049a5873756f083511bd4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a797d968ceec48aa87117f69f66c111a", "style": "IPY_MODEL_750f062a54b34bc294cf1b58f4b0b591", "value": "<div>Check ran on 805 data samples. Mean IoU on the full data set is 0.2.</div>"}}, "a797d968ceec48aa87117f69f66c111a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "750f062a54b34bc294cf1b58f4b0b591": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a4974404b3ef42bc8f7351ecf1bfb29a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5b0b117febe49daa5bc9a4e5651e012": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f4200cd413d4444a920a3999041097cc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "89656046f3a94b2d9f3ea11aae232ae4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5dd621b9ba5d4c5a9b3119dd2ed18e29", "style": "IPY_MODEL_da76a3e1149d426eab5a4067e5fa8213", "value": "<br><a href=\"#summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-U7ONZ\" style=\"font-size: 14px;\">Go to top</a>"}}, "5dd621b9ba5d4c5a9b3119dd2ed18e29": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da76a3e1149d426eab5a4067e5fa8213": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9134ea8652cd4afaaebcbc4c3b88742e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6b6b32cbbfa7401db8e9e994ad6f0829": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5bc0e0e3183e4158b14ecfc4d5d6a986": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9fe7f0fc9306495e81dae2b859fe9533": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ddb9721e113c42dc9de59392cd700904": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b082930f7e2450682f13790b0f51ca8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3bc1bf3e86074220834cd730b7baf8ab", "IPY_MODEL_04282ef6cef6409696fc820a11bd2dbf"], "layout": "IPY_MODEL_2bbdf0a095b4435295edb1e000aa122b"}}, "3bc1bf3e86074220834cd730b7baf8ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f16d179c6a2b4f9e9fc6c28f802c2b4d", "style": "IPY_MODEL_8db135f306ad427387e2ffe2a3915480", "value": "<span id=\"summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-3JBCK\"></span>"}}, "f16d179c6a2b4f9e9fc6c28f802c2b4d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8db135f306ad427387e2ffe2a3915480": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "04282ef6cef6409696fc820a11bd2dbf": {"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_548da8fd3c2246ff86cf78228badf4fd"], "layout": "IPY_MODEL_78c02c2059634fa985094f3f489a117f", "selected_index": null}}, "548da8fd3c2246ff86cf78228badf4fd": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_eda9a6618e3246a9af12304c0cb1a4fb", "IPY_MODEL_1affa749db2f43c1af313daac00aebe1", "IPY_MODEL_8c1169906bf54085a715558de71cb26d"], "layout": "IPY_MODEL_3b58de7573b54f109f064c260c895a00"}}, "eda9a6618e3246a9af12304c0cb1a4fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a088c0c06fce458dab83bcf155e3e7ae", "style": "IPY_MODEL_64d0ae9ebe144fffa6995fdac09bfec4", "value": "<style type=\"text/css\">\n#T_8f2e4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8f2e4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8f2e4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8f2e4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8f2e4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8f2e4_\">\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_8f2e4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8f2e4_row0_col1\" class=\"data row0 col1\" ><a href=\"#PredictionDrift_DSC4E5HRDU4XSZJNVC7HR6C2C-section-3JBCK\">Prediction Drift</a></td>\n      <td id=\"T_8f2e4_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_8f2e4_row0_col3\" class=\"data row0 col3\" >Passed for 3 prediction properties out of 3 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0\nFound prediction property \"Bounding Box Area (in pixels)\" has the highest numerical drift score: 0.11</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a088c0c06fce458dab83bcf155e3e7ae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64d0ae9ebe144fffa6995fdac09bfec4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1affa749db2f43c1af313daac00aebe1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c9b85846f1cd490f83acb711ea670dff", "style": "IPY_MODEL_048d120b6e17460bad61f3d399f15c3e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c9b85846f1cd490f83acb711ea670dff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "048d120b6e17460bad61f3d399f15c3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8c1169906bf54085a715558de71cb26d": {"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_66f01d67d1f2447da5ccc86490ae628f", "IPY_MODEL_db89ce7cd6d743bbb92f66da0d99570d", "IPY_MODEL_6c05aa2038e44a54b9664ceb5198c2f0", "IPY_MODEL_9179cb9ba0e743b2a52bd72025100a78"], "layout": "IPY_MODEL_3c3a57ffafb54b61ad6b2935f07fef74"}}, "66f01d67d1f2447da5ccc86490ae628f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d5e20b2ee16f4b5ca878783844241816", "style": "IPY_MODEL_f6fd272b16eb431795f24eb1cd81163e", "value": "<h4 id=\"PredictionDrift_DSC4E5HRDU4XSZJNVC7HR6C2C-section-3JBCK\"><b>Prediction Drift</b></h4>"}}, "d5e20b2ee16f4b5ca878783844241816": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f6fd272b16eb431795f24eb1cd81163e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "db89ce7cd6d743bbb92f66da0d99570d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d6f67275faaf416fa3c41ba7e391aff0", "style": "IPY_MODEL_c2e441ca93f64dbd9bd809767c20ddd4", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d6f67275faaf416fa3c41ba7e391aff0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c2e441ca93f64dbd9bd809767c20ddd4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6c05aa2038e44a54b9664ceb5198c2f0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d5b5db4f090d446a9db0e9591261c923", "style": "IPY_MODEL_4b03b8f1fbe54f3fb3ecff4decdcf714", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a241c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a241c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a241c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a241c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a241c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a241c_\">\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_a241c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a241c_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_a241c_row0_col2\" class=\"data row0 col2\" >Passed for 3 prediction properties out of 3 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0\nFound prediction property \"Bounding Box Area (in pixels)\" has the highest numerical drift score: 0.11</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d5b5db4f090d446a9db0e9591261c923": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b03b8f1fbe54f3fb3ecff4decdcf714": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9179cb9ba0e743b2a52bd72025100a78": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c1641dc0b10e46bab172dc0ec9e308c8", "IPY_MODEL_700cefd0ca674449ad7bc524fa2fb2af", "IPY_MODEL_863ddef2707342a1b4674fab9f9842b3", "IPY_MODEL_fc533ede2ffc4fc99dc704d1c5f1d000", "IPY_MODEL_1e356f6a050c499c840534d2b3ea7c05", "IPY_MODEL_f168170d4ecb45c09248cdfdf6281f80", "IPY_MODEL_0f1f58a13eb940ed90143e3b07ac68c5"], "layout": "IPY_MODEL_7431f1e465304150ad4d9b569833bcd1"}}, "c1641dc0b10e46bab172dc0ec9e308c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ec641cac45a14269bbf3e02649745cfd", "style": "IPY_MODEL_2c00c13a9d7748c6a05bcf697ad3afc6", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ec641cac45a14269bbf3e02649745cfd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c00c13a9d7748c6a05bcf697ad3afc6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "700cefd0ca674449ad7bc524fa2fb2af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9783aedcd4f146db8c509fc08d02bb00", "style": "IPY_MODEL_286ab88da0f84d37b1d6c1d713129002", "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', 'Bounding Box Area (in pixels)', 'Number of Bounding Boxes Per Image']. </span></div>"}}, "9783aedcd4f146db8c509fc08d02bb00": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "286ab88da0f84d37b1d6c1d713129002": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "863ddef2707342a1b4674fab9f9842b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3bb21cb6d45497591a6ac93a510eb76", "style": "IPY_MODEL_1fe509f41331441ea2c4b9e156208f2f", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "b3bb21cb6d45497591a6ac93a510eb76": {"model_name": 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"model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8b163c1724c94ce2823b64deb5bea269", "IPY_MODEL_88d24baed88343ad8b9b30d864f6a018", "IPY_MODEL_5d69afbacf3d4a94a624478b1142d45d", "IPY_MODEL_e6f82fda87774abdb5b829dfaa50bf73", "IPY_MODEL_e0476f45e5d34fc0aaf2a7340b0a6614", "IPY_MODEL_e6f82fda87774abdb5b829dfaa50bf73", "IPY_MODEL_a8721d4b1aa24cbb87d3b3601aebc156", "IPY_MODEL_e6f82fda87774abdb5b829dfaa50bf73", "IPY_MODEL_5d2be62d465141b8b35ea211e2ebccea"], "layout": "IPY_MODEL_b5b0fd2215e84380abcdfb8f9a30c033"}}, "8b163c1724c94ce2823b64deb5bea269": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1d30fc2d3c11448885e628d2f4ba9cbe", "style": "IPY_MODEL_3dbd50c117d44e24bfdfac8b899acb49", "value": "<style type=\"text/css\">\n#T_2acfa_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2acfa_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2acfa_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2acfa_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2acfa_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2acfa_\">\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_2acfa_row0_col0\" class=\"data row0 col0\" ><a href=\"#MeanAverageRecallReport_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\">Mean Average Recall Report</a></td>\n      <td id=\"T_2acfa_row0_col1\" class=\"data row0 col1\" >Summarize mean average recall metrics on a dataset and model per detections and area range. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_mean_average_recall_report.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_2acfa_row1_col0\" class=\"data row1 col0\" ><a href=\"#MeanAverageRecallReport_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\">Mean Average Recall Report</a></td>\n      <td id=\"T_2acfa_row1_col1\" class=\"data row1 col1\" >Summarize mean average recall metrics on a dataset and model per detections and area range. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_mean_average_recall_report.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_2acfa_row2_col0\" class=\"data row2 col0\" ><a href=\"#ConfusionMatrix_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\">Confusion Matrix</a></td>\n      <td id=\"T_2acfa_row2_col1\" class=\"data row2 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/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_2acfa_row3_col0\" class=\"data row3 col0\" ><a href=\"#ConfusionMatrix_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\">Confusion Matrix</a></td>\n      <td id=\"T_2acfa_row3_col1\" class=\"data row3 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/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  </tbody>\n</table>\n"}}, "1d30fc2d3c11448885e628d2f4ba9cbe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3dbd50c117d44e24bfdfac8b899acb49": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "88d24baed88343ad8b9b30d864f6a018": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c7964b1828c44d14b6fc7458897fc80d", "style": "IPY_MODEL_4b14be474449485d85d1c12d05a9de2b", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c7964b1828c44d14b6fc7458897fc80d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b14be474449485d85d1c12d05a9de2b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d69afbacf3d4a94a624478b1142d45d": {"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_a39e0c54ffb54b3eadb2ea5ad1dce4cb", "IPY_MODEL_3eb132e7b6d5403a9b4c0bf2d472f481", "IPY_MODEL_3238a2b50f894f5d8d609923b42eb914"], "layout": "IPY_MODEL_189b57bec2e9430abe409b59d255910b"}}, "a39e0c54ffb54b3eadb2ea5ad1dce4cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c09c4018a3dc4a19ad8c65f6db9a2ff5", "style": "IPY_MODEL_15b3e473ab754c4ea1abed2eafaec370", "value": "<h4 id=\"MeanAverageRecallReport_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\"><b>Mean Average Recall Report</b></h4>"}}, "c09c4018a3dc4a19ad8c65f6db9a2ff5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "15b3e473ab754c4ea1abed2eafaec370": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3eb132e7b6d5403a9b4c0bf2d472f481": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a66c05a931a94db0a09adf35db5203fb", "style": "IPY_MODEL_f8c2772364c9444fa29c2fac9881d67b", "value": "<p>Summarize mean average recall metrics on a dataset and model per detections and area range. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_mean_average_recall_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a66c05a931a94db0a09adf35db5203fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f8c2772364c9444fa29c2fac9881d67b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3238a2b50f894f5d8d609923b42eb914": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_91a6f98626ba4b4cbccfc69bebf05c79", "IPY_MODEL_c2ed85d030594cc3bd641cfc7db0667c", "IPY_MODEL_fa3d368d532f4a09a477e009f5c960e5"], "layout": "IPY_MODEL_7f4b8e32627b48d7ba4741ce3d715bf1"}}, "91a6f98626ba4b4cbccfc69bebf05c79": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_39af79e1082d473da985203f95ab54fe", "style": "IPY_MODEL_61296ff760414f1ba6f5db61c87c1334", "value": "<h5><b>Additional Outputs</b></h5>"}}, "39af79e1082d473da985203f95ab54fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61296ff760414f1ba6f5db61c87c1334": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c2ed85d030594cc3bd641cfc7db0667c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e6ba119432ae4b3fa26b81903969a399", "style": "IPY_MODEL_8df72a3f3529484f8e7ddcb9db55e226", "value": "<style type=\"text/css\">\n#T_58e22_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_58e22_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_58e22_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_58e22_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_58e22_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_58e22_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >AR@1 (%)</th>\n      <th class=\"col_heading level0 col1\" >AR@10 (%)</th>\n      <th class=\"col_heading level0 col2\" >AR@100 (%)</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Area size</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_58e22_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n      <td id=\"T_58e22_row0_col0\" class=\"data row0 col0\" >0.02</td>\n      <td id=\"T_58e22_row0_col1\" class=\"data row0 col1\" >0.04</td>\n      <td id=\"T_58e22_row0_col2\" class=\"data row0 col2\" >0.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_58e22_level0_row1\" class=\"row_heading level0 row1\" >Small (area < 32^2)</th>\n      <td id=\"T_58e22_row1_col0\" class=\"data row1 col0\" >0.00</td>\n      <td id=\"T_58e22_row1_col1\" class=\"data row1 col1\" >0.01</td>\n      <td id=\"T_58e22_row1_col2\" class=\"data row1 col2\" >0.01</td>\n    </tr>\n    <tr>\n      <th id=\"T_58e22_level0_row2\" class=\"row_heading level0 row2\" >Medium (32^2 < area < 96^2)</th>\n      <td id=\"T_58e22_row2_col0\" class=\"data row2 col0\" >0.07</td>\n      <td id=\"T_58e22_row2_col1\" class=\"data row2 col1\" >0.17</td>\n      <td id=\"T_58e22_row2_col2\" class=\"data row2 col2\" >0.17</td>\n    </tr>\n    <tr>\n      <th id=\"T_58e22_level0_row3\" class=\"row_heading level0 row3\" >Large (area < 96^2)</th>\n      <td id=\"T_58e22_row3_col0\" class=\"data row3 col0\" >0.00</td>\n      <td id=\"T_58e22_row3_col1\" class=\"data row3 col1\" >0.00</td>\n      <td id=\"T_58e22_row3_col2\" class=\"data row3 col2\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e6ba119432ae4b3fa26b81903969a399": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8df72a3f3529484f8e7ddcb9db55e226": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa3d368d532f4a09a477e009f5c960e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5154fd1ca19348269052222dd5009f54", "style": "IPY_MODEL_afdd0cecb65942c08b9d220df7a1ca56", "value": "<br><a href=\"#summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\" style=\"font-size: 14px;\">Go to top</a>"}}, "5154fd1ca19348269052222dd5009f54": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "afdd0cecb65942c08b9d220df7a1ca56": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f4b8e32627b48d7ba4741ce3d715bf1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "189b57bec2e9430abe409b59d255910b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e6f82fda87774abdb5b829dfaa50bf73": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_26bd7c3da31d4208bc010b5761c8a45c", "style": "IPY_MODEL_3edfe3527936409a94be0475aa04b367", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "26bd7c3da31d4208bc010b5761c8a45c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3edfe3527936409a94be0475aa04b367": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e0476f45e5d34fc0aaf2a7340b0a6614": {"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_4c694188cc974f70b8f313190b6c22dd", "IPY_MODEL_fbe7af36778841f6bf005fbc441701c8", "IPY_MODEL_1cc194db41904a57bcbce46ef38b3af4"], "layout": "IPY_MODEL_ec7384507b274ce9b75c8fe47124d0ca"}}, "4c694188cc974f70b8f313190b6c22dd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6f534ecb0c934e38bdaf9ff61ac6fc52", "style": "IPY_MODEL_06dd66954ad04ac7ba88cde95c115a31", "value": "<h4 id=\"MeanAverageRecallReport_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\"><b>Mean Average Recall Report</b></h4>"}}, "6f534ecb0c934e38bdaf9ff61ac6fc52": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "06dd66954ad04ac7ba88cde95c115a31": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fbe7af36778841f6bf005fbc441701c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e93e48dfb09a4eaa951eaa55407125bd", "style": "IPY_MODEL_88c5be183c6845538b01b4db204ac0cc", "value": "<p>Summarize mean average recall metrics on a dataset and model per detections and area range. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_mean_average_recall_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e93e48dfb09a4eaa951eaa55407125bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88c5be183c6845538b01b4db204ac0cc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1cc194db41904a57bcbce46ef38b3af4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b5d3a292d01142e39d69548f42b06995", "IPY_MODEL_e022bccb1b344d91b97d2b0d29f532c8", "IPY_MODEL_bb21795fbcfe4267a8f1d7d8fe7112e9"], "layout": "IPY_MODEL_70c472efbb8e48539acacf4dd8464955"}}, "b5d3a292d01142e39d69548f42b06995": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_05ce2ececf0c4f35a033ad21aaf5c06e", "style": "IPY_MODEL_e42dc62d93f84bf69364263c6c67e273", "value": "<h5><b>Additional Outputs</b></h5>"}}, "05ce2ececf0c4f35a033ad21aaf5c06e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e42dc62d93f84bf69364263c6c67e273": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e022bccb1b344d91b97d2b0d29f532c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dec3969c3c3446e0b44ecd6eb6c40297", "style": "IPY_MODEL_c7f5bc6a250f46ed93b6e354926407f0", "value": "<style type=\"text/css\">\n#T_1c65d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1c65d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1c65d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1c65d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1c65d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_1c65d_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >AR@1 (%)</th>\n      <th class=\"col_heading level0 col1\" >AR@10 (%)</th>\n      <th class=\"col_heading level0 col2\" >AR@100 (%)</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Area size</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1c65d_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n      <td id=\"T_1c65d_row0_col0\" class=\"data row0 col0\" >0.02</td>\n      <td id=\"T_1c65d_row0_col1\" class=\"data row0 col1\" >0.06</td>\n      <td id=\"T_1c65d_row0_col2\" class=\"data row0 col2\" >0.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c65d_level0_row1\" class=\"row_heading level0 row1\" >Small (area < 32^2)</th>\n      <td id=\"T_1c65d_row1_col0\" class=\"data row1 col0\" >0.00</td>\n      <td id=\"T_1c65d_row1_col1\" class=\"data row1 col1\" >0.01</td>\n      <td id=\"T_1c65d_row1_col2\" class=\"data row1 col2\" >0.01</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c65d_level0_row2\" class=\"row_heading level0 row2\" >Medium (32^2 < area < 96^2)</th>\n      <td id=\"T_1c65d_row2_col0\" class=\"data row2 col0\" >0.09</td>\n      <td id=\"T_1c65d_row2_col1\" class=\"data row2 col1\" >0.22</td>\n      <td id=\"T_1c65d_row2_col2\" class=\"data row2 col2\" >0.22</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c65d_level0_row3\" class=\"row_heading level0 row3\" >Large (area < 96^2)</th>\n      <td id=\"T_1c65d_row3_col0\" class=\"data row3 col0\" >0.00</td>\n      <td id=\"T_1c65d_row3_col1\" class=\"data row3 col1\" >0.02</td>\n      <td id=\"T_1c65d_row3_col2\" class=\"data row3 col2\" >0.02</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "dec3969c3c3446e0b44ecd6eb6c40297": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c7f5bc6a250f46ed93b6e354926407f0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bb21795fbcfe4267a8f1d7d8fe7112e9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_96d44bcd97314dcdb64ffe2887526903", "style": "IPY_MODEL_d55b47a7f541481dbc4499de4c11696f", "value": "<br><a href=\"#summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\" style=\"font-size: 14px;\">Go to top</a>"}}, "96d44bcd97314dcdb64ffe2887526903": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d55b47a7f541481dbc4499de4c11696f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "70c472efbb8e48539acacf4dd8464955": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec7384507b274ce9b75c8fe47124d0ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a8721d4b1aa24cbb87d3b3601aebc156": {"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_59d3ce270963456e90828f58f5572079", "IPY_MODEL_38878f61b6c644de87492b25611a4d7e", "IPY_MODEL_f5c6d9c97f5f4eb385dd47ab32425b3a"], "layout": "IPY_MODEL_65595ded21bb4568bfe800e1b6957f4c"}}, "59d3ce270963456e90828f58f5572079": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_562cc470bd924e3b8f811a9c87504b5c", "style": "IPY_MODEL_9e8bc2d5ae134b3cb20e03ae7033c78e", "value": "<h4 id=\"ConfusionMatrix_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\"><b>Confusion Matrix</b></h4>"}}, "562cc470bd924e3b8f811a9c87504b5c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9e8bc2d5ae134b3cb20e03ae7033c78e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "38878f61b6c644de87492b25611a4d7e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_92ef22703e92417e96b4134df1d6c55d", "style": "IPY_MODEL_7cfd036349ff4033a9f758893f953a1e", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "92ef22703e92417e96b4134df1d6c55d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7cfd036349ff4033a9f758893f953a1e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f5c6d9c97f5f4eb385dd47ab32425b3a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ec4d69dc22ce410c96d27bd74fcb6e00", "IPY_MODEL_e32c0d4997974afd9a3686a21f58519e", "IPY_MODEL_c64ec812c9204ef69e7ecb6f59d7abc2", "IPY_MODEL_ed97fa7694c343efa049895127dc120c", "IPY_MODEL_caac776aaa77486fad1e61f77d5f8499", "IPY_MODEL_6b44eaafdf3f4479bfdf9e59e2b380f2"], "layout": "IPY_MODEL_26994c093d6d43519890342bb79f1506"}}, "ec4d69dc22ce410c96d27bd74fcb6e00": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_37414a0a268740babdb40d0beb614cbe", "style": "IPY_MODEL_3cf82df4cb39435d9133cc7b68f7ae0b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "37414a0a268740babdb40d0beb614cbe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3cf82df4cb39435d9133cc7b68f7ae0b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e32c0d4997974afd9a3686a21f58519e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9a9353d0f629463e9ce1f476b40ac180", "style": "IPY_MODEL_e29ff8b879ed42b98f0b35ab8d0cc519", "value": "<div>Showing 10 of 1 classes:</div>"}}, "9a9353d0f629463e9ce1f476b40ac180": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e29ff8b879ed42b98f0b35ab8d0cc519": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c64ec812c9204ef69e7ecb6f59d7abc2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b262ae151b947c094dc1a73fa22570a", "style": "IPY_MODEL_ce848e17119c47f8b3e00b265ac4e581", "value": "<div>\"No overlapping\" categories are labels and prediction which did not have a matching label/prediction.<br>For example a predictions that did not have a sufficiently overlapping label bounding box will appear under \"No overlapping\" category in the True Value axis (y-axis).</div>"}}, "2b262ae151b947c094dc1a73fa22570a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ce848e17119c47f8b3e00b265ac4e581": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed97fa7694c343efa049895127dc120c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6d9820d0d9ed460395c3f27e61b39c77", "style": "IPY_MODEL_79f2586b0b5c44eda7b9655ab02938e1", "value": "<div>The overall accuracy of your model is: 5.86%.<br>Best accuracy achieved on samples with <b>Tomato</b> label (11.72%).<br>Worst accuracy achieved on samples with <b>No overlapping</b> label (0.0%).</div>"}}, "6d9820d0d9ed460395c3f27e61b39c77": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "79f2586b0b5c44eda7b9655ab02938e1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "caac776aaa77486fad1e61f77d5f8499": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[9.76, 11.72, 36.84], [73.51, 88.28, 277.44]], [[16.73, 100.0, 22.76], [0.0, 0.0, 0.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[49.0, 369.0], [84.0, 0.0]], "texttemplate": "%{text}", "x": ["Tomato", "No overlapping"], "xgap": 1, "y": ["Tomato", "No overlapping"], "ygap": 1, "z": [["11.72", "88.28"], ["100", "0"]], "type": "heatmap", "uid": "d07bb61c-7d18-43ca-b0ae-64b43f8b5574"}], "_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, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#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": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "6b44eaafdf3f4479bfdf9e59e2b380f2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ebb6fcbc8e0742ec963d28073b8e5ac1", "style": "IPY_MODEL_4340ee29bb784fe98c1037b211100984", "value": "<br><a href=\"#summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\" style=\"font-size: 14px;\">Go to top</a>"}}, "ebb6fcbc8e0742ec963d28073b8e5ac1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4340ee29bb784fe98c1037b211100984": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "26994c093d6d43519890342bb79f1506": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "65595ded21bb4568bfe800e1b6957f4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d2be62d465141b8b35ea211e2ebccea": {"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_32b83c77e3a64c7c9c7586fcbc53d4c8", "IPY_MODEL_2b09ab650b0f47dda5f60d9694079e82", "IPY_MODEL_e10373a74ae14dde9f9918007f6fafd2"], "layout": "IPY_MODEL_66c85326d98a4565b9f110f4a1654a90"}}, "32b83c77e3a64c7c9c7586fcbc53d4c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_02de26a738b3465db3bfc29f47e9360b", "style": "IPY_MODEL_ad51ece9718d4d7aa0e0776201462f09", "value": "<h4 id=\"ConfusionMatrix_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\"><b>Confusion Matrix</b></h4>"}}, "02de26a738b3465db3bfc29f47e9360b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad51ece9718d4d7aa0e0776201462f09": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2b09ab650b0f47dda5f60d9694079e82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0913332b3335400bb76d886f6eff3d89", "style": "IPY_MODEL_2e5345fb299e4497ac0566e46f5f1bbb", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "0913332b3335400bb76d886f6eff3d89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2e5345fb299e4497ac0566e46f5f1bbb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e10373a74ae14dde9f9918007f6fafd2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_295846dd7b164672978be9deb39167b5", "IPY_MODEL_80b2707374ed4e6baffed002bd2f4c59", "IPY_MODEL_b107c25070b14e028402d9df727fd7e9", "IPY_MODEL_54b3c27d99dc4cc3b1d422b02f2444be", "IPY_MODEL_c0fc7aca2abc4e92a60529275c9cf5bd", "IPY_MODEL_a5bb6bf2966143daaa40f0a831ce2c91"], "layout": "IPY_MODEL_a99a420c53f444f3bcbb4c2c0affd8ef"}}, "295846dd7b164672978be9deb39167b5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_48ca30578d634ffaab00f1983cdfe26f", "style": "IPY_MODEL_ffad8f2819264b6682aac2652ed9a5c4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "48ca30578d634ffaab00f1983cdfe26f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ffad8f2819264b6682aac2652ed9a5c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "80b2707374ed4e6baffed002bd2f4c59": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8641153576c8451784c58c93f01203c7", "style": "IPY_MODEL_707c313d510846dd99a4472a6afbb4fa", "value": "<div>Showing 10 of 1 classes:</div>"}}, "8641153576c8451784c58c93f01203c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "707c313d510846dd99a4472a6afbb4fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b107c25070b14e028402d9df727fd7e9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7de8fee523fb4e2e8a352e680b830cad", "style": "IPY_MODEL_88894974862f4caf9f0efe9d2b8cd799", "value": "<div>\"No overlapping\" categories are labels and prediction which did not have a matching label/prediction.<br>For example a predictions that did not have a sufficiently overlapping label bounding box will appear under \"No overlapping\" category in the True Value axis (y-axis).</div>"}}, "7de8fee523fb4e2e8a352e680b830cad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88894974862f4caf9f0efe9d2b8cd799": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "54b3c27d99dc4cc3b1d422b02f2444be": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_16325b05971947a2801b3ecaff06c41a", "style": "IPY_MODEL_eb596e1ce09042e3a9795f8f7f919645", "value": "<div>The overall accuracy of your model is: 6.58%.<br>Best accuracy achieved on samples with <b>Tomato</b> label (13.17%).<br>Worst accuracy achieved on samples with <b>No overlapping</b> label (0.0%).</div>"}}, "16325b05971947a2801b3ecaff06c41a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eb596e1ce09042e3a9795f8f7f919645": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c0fc7aca2abc4e92a60529275c9cf5bd": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[10.66, 13.17, 35.83], [70.26, 86.83, 236.24]], [[19.08, 100.0, 27.16], [0.0, 0.0, 0.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[535.0, 3527.0], [958.0, 0.0]], "texttemplate": "%{text}", "x": ["Tomato", "No overlapping"], "xgap": 1, "y": ["Tomato", "No overlapping"], "ygap": 1, "z": [["13.17", "86.83"], ["100", "0"]], "type": "heatmap", "uid": "c6889d45-c0f7-470e-b7b3-4d4441c76375"}], "_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": 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"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"], 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"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": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "a5bb6bf2966143daaa40f0a831ce2c91": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c24b35cc45a74ee78616fa529fe7849a", "style": "IPY_MODEL_1cfbcfa34f0044b79e80319b01dfbd6f", "value": "<br><a href=\"#summary_DSC4E5HRDU4XSZJNVC7HR6C2C-section-88D0T\" style=\"font-size: 14px;\">Go to top</a>"}}, "c24b35cc45a74ee78616fa529fe7849a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": 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    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 335-341

We can see that our model does not perform well, as can be seen in the "Class Performance" check under the
"Didn't Pass" section of the suite results. This is because the model was trained on a different dataset, and
the model was not trained to detect tomatoes.
Moreover, we can see that lowering the IoU threshold could have fixed this a bit (as can be seen in the
"Mean Average Precision Report" Check), but would still keep the overall precision low.
Moreover, under the "Passed" section, we can see that our drift checks have passed, which means that the distribution
of the predictions on the training and test data is similar, and the issue is not there but in the model itself.


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

   **Total running time of the script:** (0 minutes 51.047 seconds)


.. _sphx_glr_download_vision_auto_tutorials_quickstarts_plot_detection_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_detection_tutorial.py <plot_detection_tutorial.py>`

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

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