Note
Go to the end to download the full example code
Confusion Matrix#
This notebooks provides an overview for using and understanding the confusion matrix check.
Structure:
What is the purpose of the check?#
The confusion matrix check outputs a confusion matrix for both classification problems and object detection problems. In object detection problems, some predictions do not overlap on any label and can be classified as not found in the confusion matrix.
Generate Dataset#
We generate a sample dataset of 128 images from the COCO dataset, and using the YOLOv5 model.
Note
In this example, we use the pytorch version of the coco dataset and model. In order to run this example using tensorflow, please change the import statements to:
from deepchecks.vision.datasets.detection import coco_tensorflow as coco
from deepchecks.vision.datasets.detection import coco_torch as coco
train_ds = coco.load_dataset(object_type='VisionData')
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...
0%| | 0.00/14.1M [00:00<?, ?B/s]
73%|███████▎ | 10.4M/14.1M [00:00<00:00, 108MB/s]
100%|██████████| 14.1M/14.1M [00:00<00:00, 117MB/s]
Run the check#
from deepchecks.vision.checks import ConfusionMatrixReport
check = ConfusionMatrixReport(categories_to_display=10)
result = check.run(train_ds)
result
Processing Batches:
| | 0/1 [Time: 00:00]
Processing Batches:
|█████| 1/1 [Time: 00:00]
Processing Batches:
|█████| 1/1 [Time: 00:00]
Computing Check:
| | 0/1 [Time: 00:00]
Computing Check:
|█████| 1/1 [Time: 00:00]
To display the results in an IDE like PyCharm, you can use the following code:
# result.show_in_window()
The result will be displayed in a new window.
Total running time of the script: (0 minutes 1.791 seconds)