feat: Added torchvision photometric augmentations in artefact detection training

[SiddhantBahuguna]

This PR makes the following modifications to the existing repo:

1. It adds torchvision photometric augmentations : ColorJitter & GaussianBlur for the training of artefact detection.
2. Updates a checkpoint with improved recall & segmentation on test data.

Summary:

Before augmentation:
mean precision 0.57
mean seg 0.62
mean recall 0.82

After augmentation:
mean precision 0.51
mean seg 0.67
mean recall 0.89

Any feedback is welcome!

[SiddhantBahuguna]

Awaiting for the new checkout and the potential improvement in the results on the real world data before merging this PR.

[fg-mindee]

Thanks a lot!
Could you run a full training with this PR before we merge it?

So that we can add the new checkpoint in the PR if it improves performances 👌

[fg-mindee]

Thanks! Mind adding the performance changes in the PR please?

[SiddhantBahuguna]

Thanks! Mind adding the performance changes in the PR please?

Sure, although I have added recall that matters the most to us. I will include other metrics as well.

[codecov]

Codecov Report

Merging #764 (fc64cca) into main (1dc3374) will decrease coverage by 0.02%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##             main     #764      +/-   ##
==========================================
- Coverage   96.02%   96.00%   -0.03%     
==========================================
  Files         129      129              
  Lines        4807     4807              
==========================================
- Hits         4616     4615       -1     
- Misses        191      192       +1     

Flag	Coverage Δ
unittests	96.00% <ø> (-0.03%)	⬇️

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Impacted Files	Coverage Δ
doctr/models/obj_detection/faster_rcnn/pytorch.py	100.00% <ø> (ø)
doctr/models/builder.py	96.07% <0.00%> (-0.99%)	⬇️
doctr/transforms/modules/base.py	94.44% <0.00%> (ø)

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