Co-DETR vs Detectron2

Co-DETR (Co-Deformable-DETR) is an object detection model developed by researchers at Sense-X and OpenMMLab, released in November 2022. It improves upon standard DETR-based detectors by introducing a collaborative hybrid assignment training scheme that enables the encoder to learn from multiple auxiliary heads simultaneously, alongside the primary one-to-one assignment used during inference. This auxiliary supervision significantly accelerates convergence and improves overall detection accuracy without adding inference cost.

Co-DETR is evaluated on the COCO benchmark, where it achieves 59.5% AP when applied to DINO-Deformable-DETR with a Swin-L backbone. With a ViT-L backbone it reaches 66.0% AP on COCO test-dev, outperforming prior methods at comparable model scales. It is suitable for high-accuracy object detection tasks where training efficiency and peak performance on standard benchmarks are priorities.

Detectron2 is a computer vision model library developed by Facebook AI Research (Meta), released in September 2019. It serves as a comprehensive platform for object detection, instance segmentation, panoptic segmentation, keypoint detection, and DensePose, implemented in PyTorch. It is the successor to the original Detectron framework, which was written in Caffe2, and offers a more modular and extensible codebase designed for both research and production use.

Detectron2 includes implementations of Faster R-CNN, Mask R-CNN, RetinaNet, Cascade R-CNN, Panoptic FPN, and several other architectures. Its modular design allows components such as backbones, necks, and heads to be swapped independently, making it widely used as a baseline framework in academic research. It supports training on COCO-format datasets and integrates with standard distributed training setups.