| --- |
| 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: [2.5.3] |
| - Cooper Foundry: [2.2] |
|
|
| | Model | Device | compression | Model Link | |
| | :-----: | :-----: | :-----: | ------- | |
| | YOLOX-s | N1-655 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/n1-655_yolox_s_amba_optimized.bin) | |
| | YOLOX-s | N1-655 | Activation_fp16 | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/n1-655_yolox_s_act16.bin) | |
| | YOLOX-s | CV7 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv7_yolox_s_amba_optimized.bin) | |
| | YOLOX-s | CV7 | Activation_fp16 | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv7_yolox_s_act16.bin) | |
| | YOLOX-s | CV72 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv72_yolox_s_amba_optimized.bin) | |
| | YOLOX-s | CV72 | Activation_fp16 | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv72_yolox_s_act16.bin) | |
| | YOLOX-s | CV75 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv75_yolox_s_amba_optimized.bin) | |
| | YOLOX-s | CV75 | Activation_fp16 | [Model_Link](https://huggingface.co/Ambarella/YOLOX/blob/main/cv75_yolox_s_act16.bin) | |
|
|
