feat: Added checkpoint for obj_detection

[SiddhantBahuguna]

This PR adds a checkpoint for artefact detection.
Any feedback is welcome:)

[fg-mindee]

Thanks!

Would you mind modifying the unittest https://github.com/mindee/doctr/blob/main/tests/pytorch/test_models_obj_detection_pt.py
with:

• reducing the input size to (512, 512) to ease the RAM
• before line 15, recreate the model by passing pretrained=True to ensure the checkpoint can be downloaded

[SiddhantBahuguna]

Thanks!

Would you mind modifying the unittest https://github.com/mindee/doctr/blob/main/tests/pytorch/test_models_obj_detection_pt.py with:

* reducing the input size to (512, 512) to ease the RAM

* before line 15, recreate the model by passing `pretrained=True` to ensure the checkpoint can be downloaded

Thanks. I have made the changes .

[codecov]

Codecov Report

Merging #713 (60dd114) into main (c32c1ed) will increase coverage by 0.08%.
The diff coverage is n/a.

❗ Current head 60dd114 differs from pull request most recent head f20e299. Consider uploading reports for the commit f20e299 to get more accurate results

@@            Coverage Diff             @@
##             main     #713      +/-   ##
==========================================
+ Coverage   96.23%   96.32%   +0.08%     
==========================================
  Files         125      125              
  Lines        4707     4707              
==========================================
+ Hits         4530     4534       +4     
+ Misses        177      173       -4     

Flag	Coverage Δ
unittests	96.32% <ø> (+0.08%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
doctr/models/obj_detection/faster_rcnn/pytorch.py	87.50% <ø> (-6.25%)	⬇️
doctr/models/builder.py	99.12% <0.00%> (+2.63%)	⬆️
doctr/models/recognition/predictor/pytorch.py	96.96% <0.00%> (+6.06%)	⬆️

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update c32c1ed...f20e299. Read the comment docs.

[fg-mindee]

Last suggestion to check both cases (especially for faster, pretrained=True does change a lot the initialization of the model)

[SiddhantBahuguna]

Last suggestion to check both cases (especially for faster, pretrained=True does change a lot the initialization of the model)

Thanks for all the suggestions :) Implemented them all

[SiddhantBahuguna]

I think it had passed the unittest for object detection. It should not be a problem right?
A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. Error: Process completed with exit code 1.

[fg-mindee]

Looks good thanks!

[fg-mindee]

All good, the CI is having timeouts on datasets unittests, but that will be fixed by another PR quite soon anyway!

[SiddhantBahuguna]

All good, the CI is having timeouts on datasets unittests, but that will be fixed by another PR quite soon anyway!

Thanks for the information :)