Co-DETR vs YOLOS

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.

YOLOS (You Only Look at One Sequence) is a transformer-based object detection model widely distributed through Hugging Face Transformers, released in June 2021 under the MIT license. It applies a minimally adapted Vision Transformer to object detection by representing both the image and detection tokens as a flat sequence processed by standard multi-head self-attention, without convolutional components or feature pyramid networks. The architecture demonstrates that detection can be performed without region proposals or multi-scale feature fusion.

YOLOS achieves moderate performance on COCO relative to purpose-built detectors, with its primary contribution being a demonstration of the transferability of ViT pre-training to detection tasks. It is most appropriate for research contexts exploring transformer-based detection architectures and for scenarios where architectural simplicity is preferred over peak accuracy.