| library_name: pytorch | |
|  | |
| YOLOX modernizes one-stage object detection by adopting an anchor-free design and decoupled classification and regression heads, improving both accuracy and convergence speed. | |
| Original paper: [YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430) | |
| # YOLOX-S | |
| YOLOX-S (Small) is a lightweight variant optimized for fast inference while maintaining competitive detection accuracy. It is well suited for real-time object detection in applications such as video analytics, robotics, and edge deployment where low latency is critical. | |
| Model Configuration: | |
| - Reference implementation: [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) | |
| - Original Weight: [YOLOX_S_Weights.COCO2017](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth) | |
| - Resolution: 3x640x640 | |
| - Support Cooper version: | |
| - Cooper SDK: [2.5.2] | |
| - Cooper Foundry: [2.2] | |
| | Model | Device | Model Link | | |
| | :-----: | :-----: | :-----: | | |
| | YOLOX-s | N1-655 | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/n1-655_yolox_s.bin) | | |
| | YOLOX-s | CV72 | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv72_yolox_s.bin) | | |
| | YOLOX-s | CV75 | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv75_yolox_s.bin) | | |