Introduction to DETR and Detectron2

DETR (DEtection TRansformer) is a revolutionary approach to object detection that leverages transformer architectures to achieve state-of-the-art results. By integrating DETR with Detectron2, a popular object detection library developed by Facebook AI Research, developers can enhance their detection workflows and utilize existing datasets and backbones effectively.

Key Features of the DETR Wrapper

• Seamless integration with Detectron2’s ecosystem.
• Supports box detection with results matching the original DETR implementation.
• Utilizes a modified data augmentation strategy to align with DETR’s original methods.
• Custom gradient clipping mode for improved training stability.

Technical Architecture and Implementation

The DETR wrapper for Detectron2 is designed to maintain fidelity to the original DETR implementation while providing the flexibility of Detectron2’s features. The architecture includes:

• Data Augmentation: The wrapper implements a custom RandomCrop augmentation to match DETR’s original data augmentation.
• Backbone Initialization: It uses ResNet50 weights trained on ImageNet, ensuring compatibility with DETR’s initialization.
• Gradient Clipping: The wrapper employs a ‘full_model’ gradient clipping mode, differing from Detectron2’s default settings.

Installation Process

To get started with the DETR wrapper, you first need to install Detectron2. Follow the official installation instructions.

Usage Examples

Once installed, you can evaluate and train models using the following commands:

Evaluating a Model

python converter.py --source_model https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth --output_model converted_model.pth

To evaluate the converted model, run:

python train_net.py --eval-only --config configs/detr_256_6_6_torchvision.yaml  MODEL.WEIGHTS "converted_model.pth"

Training a Model

To train DETR on a single node with 8 GPUs, use:

python train_net.py --config configs/detr_256_6_6_torchvision.yaml --num-gpus 8

For fine-tuning for instance segmentation:

python train_net.py --config configs/detr_segm_256_6_6_torchvision.yaml --num-gpus 8 MODEL.DETR.FROZEN_WEIGHTS <model_path>

Community and Contribution

The DETR project encourages contributions from the community. To contribute:

• Fork the repository and create a new branch.
• Add tests for any new code.
• Update documentation for any API changes.
• Ensure the test suite passes and code adheres to the style guidelines.

For more details, refer to the Contributor License Agreement.

License and Legal Considerations

The DETR project is licensed under the Apache License 2.0. This allows for both personal and commercial use, provided that the terms of the license are followed. For more information, refer to the Apache License.

Conclusion

Integrating DETR with Detectron2 opens up new possibilities for object detection projects. With its robust architecture and community support, developers can leverage the strengths of both frameworks to build powerful detection systems.

For more information, visit the DETR GitHub repository.

Frequently Asked Questions