OTX D-Fine Detection Algorithm Integration

[eugene123tw]

Summary

OTX D-Fine Detection Algorithm Integration: https://github.com/Peterande/D-FINE

• Introduced five variants of the D-Fine detection algorithm.
• Integrated the HGNetv2 backbone from PaddleDetection.
• Cleaned and optimized the original codebase by:
  • Reducing code duplication where possible.
  • Adding docstrings for all methods and functions.
  • Benchmarking OpenVINO/PyTorch detection results for accuracy and performance.

Next phase

• Validate potential module combinations that could be unified in future iterations, such as:
  • D-Fine Decoder and RT-DETR Decoder.
  • D-Fine Hybrid Encoder and RT-DETR Decoder.
  • D-Fine Criterion and RT-DETR Criterion.
• Validate Post-Training Optimization Tool (POT) results and assess potential accuracy drops.
• Validate XAI feature.

How to test

• otx train --config src/otx/recipe/detection/dfine_x.yaml --data_root DATA_ROOT
• pytest tests/unit/algo/detection/test_dfine.py

Checklist

• I have added unit tests to cover my changes.​
• I have added integration tests to cover my changes.​
• I have ran e2e tests and there is no issues.
• I have added the description of my changes into CHANGELOG in my target branch (e.g., CHANGELOG in develop).​
• I have updated the documentation in my target branch accordingly (e.g., documentation in develop).
• I have linked related issues.

License

• I submit my code changes under the same Apache License that covers the project.
  Feel free to contact the maintainers if that's a concern.
• I have updated the license header for each file (see an example below).

# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0