Extend FakeDatasets for All Tasks

🚀 The feature

Fake Datasets help us to quickly verify and validate if the instantiated model would work fine. This enables quick validation for testing purpose as well as for faster prototyping.

We already have FakeDataset in torchvision for the same, but it supports only ImageClassification as of now.

Motivation, pitch

We should have multiple FakeData classes. Like ImageClassificationFakeData, ObjectDetectionFakeData.

If we do that, we should deprecate FakeData in favor of ImageClassificationFakeData.

We could also think of supporting different formats. E.g. Support xywh as well as xyxy format? Support formats such as Binary masks, boolean masks etc? (Not sure of this need to discuss)

Alternatives

Other libraries are maintaining something similar to test models. These libraries mostly wrap torchvision models into their framework equivalent codes and test on sample datasets.

https://github.com/Lightning-AI/lightning-bolts/blob/master/tests/models/test_detection.py

https://github.com/Lightning-AI/lightning-flash/blob/master/tests/image/detection/test_model.py

https://github.com/oke-aditya/quickvision/blob/master/tests/dataset_utils.py

Additional context

@pmeier please chip in your thoughts!

cc @pmeier

Review the existing FakeData implementation in torchvision and compare the linked detection-test examples. Clarify which task-specific dataset classes and target formats are in scope, then define completion criteria and tests for each supported task; the issue provides no file path or test entry point.