YOLOX: Exceeding YOLO Series in 2021
This paper presents a new high-performance variation of YOLO - YOLOX. Now it has an anchor-free detector, a decoupled head, and uses the leading label assignment strategy SimOTA.
Thanks to these changes, it reaches state-of-the-art results across a large scale range of models. For example, YOLOX-Nano gets 25.3% AP on COCO (+1.8% to NanoDet), YOLOX-L achieves 50.0% AP on COCO (+1.8 to YOLOv5-L).
For YOLOv3, one of the most widely used detectors in industry, they boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP.
The authors won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model.
They also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported.
Paper: https://arxiv.org/abs/2107.08430
Code: https://github.com/Megvii-BaseDetection/YOLOX
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-yolox
#deeplearning #cv #objectdetection #endtoend #anchorfree
Team
@OpenArchiveBooks
@data_entusiasts
This paper presents a new high-performance variation of YOLO - YOLOX. Now it has an anchor-free detector, a decoupled head, and uses the leading label assignment strategy SimOTA.
Thanks to these changes, it reaches state-of-the-art results across a large scale range of models. For example, YOLOX-Nano gets 25.3% AP on COCO (+1.8% to NanoDet), YOLOX-L achieves 50.0% AP on COCO (+1.8 to YOLOv5-L).
For YOLOv3, one of the most widely used detectors in industry, they boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP.
The authors won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model.
They also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported.
Paper: https://arxiv.org/abs/2107.08430
Code: https://github.com/Megvii-BaseDetection/YOLOX
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-yolox
#deeplearning #cv #objectdetection #endtoend #anchorfree
Team
@OpenArchiveBooks
@data_entusiasts
End-to-End Object Detection with Transformers
Authors present a new method that views object detection as a direct set prediction problem.
This approach simplifies the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task.
The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture
DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner
Paper: https://arxiv.org/abs/2005.12872
Code: https://github.com/facebookresearch/detr
#deeplearning #objectdetection #transformer #coco
ᅠᅠ
Team
@OpenArchiveBooks
@data_entusiasts
Authors present a new method that views object detection as a direct set prediction problem.
This approach simplifies the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task.
The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture
DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner
Paper: https://arxiv.org/abs/2005.12872
Code: https://github.com/facebookresearch/detr
#deeplearning #objectdetection #transformer #coco
ᅠᅠ
Team
@OpenArchiveBooks
@data_entusiasts