DETR vs YOLOS

DETR (Detection Transformer) is an end-to-end object detection model developed by Facebook Research (Meta), released in May 2020. It is one of the first models to eliminate hand-crafted components such as anchor generation and non-maximum suppression by framing object detection as a direct set prediction problem, solved with a transformer encoder-decoder architecture built on top of a CNN backbone.

DETR achieves 42.0% AP on the COCO benchmark with a ResNet-50 backbone, performing comparably to a well-tuned Faster R-CNN at the time of release. Its attention-based design allows it to reason about global context and long-range dependencies within an image. DETR is primarily used as a research baseline and architectural reference, with subsequent works such as Deformable DETR and DINO building on its foundations to address its slower training convergence and limited small-object detection capability.

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.