The FSAF (Feature Selective Anchor-Free) module is an innovative component for single-shot object detection that enhances performance through online feature selection and anchor-free branches. The FSAF module dynamically selects the most suitable feature level for each object instance, rather than relying on traditional anchor-based heuristic methods. This improvement significantly boosts the accuracy of object detection, especially for small targets and in complex scenes. Moreover, compared to existing anchor-based detectors, the FSAF module maintains high efficiency while adding negligible additional inference overhead.
| GPU | IXUCA SDK | Release | Branch |
|---|---|---|---|
| MR-V100 | 4.4.0 | 26.03 | release/26.03 |
| MR-V100 | 4.3.0 | 25.12 | release/25.12 |
Note: 请切换到与您的 SDK 版本对应的 Release 分支进行测试。请勿直接在 master 分支上运行测试，因为 master 分支可能包含与您的本地 SDK 版本不兼容的最新更改。
切换分支命令示例：
git checkout release/26.03
Pretrained model: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth
Dataset:
- https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels.zip to download the labels dataset.
- http://images.cocodataset.org/zips/val2017.zip to download the validation dataset.
- http://images.cocodataset.org/zips/train2017.zip to download the train dataset.
unzip -q -d ./ coco2017labels.zip
unzip -q -d ./coco/images/ train2017.zip
unzip -q -d ./coco/images/ val2017.zip
coco
├── annotations
│ └── instances_val2017.json
├── images
│ ├── train2017
│ └── val2017
├── labels
│ ├── train2017
│ └── val2017
├── LICENSE
├── README.txt
├── test-dev2017.txt
├── train2017.cache
├── train2017.txt
├── val2017.cache
└── val2017.txtwget https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pthContact the Iluvatar administrator to get the missing packages:
- mmcv-*.whl
# Install libGL
## CentOS
yum install -y mesa-libGL
## Ubuntu
apt install -y libgl1-mesa-glx
pip3 install -r requirements.txt# export onnx model
python3 export.py --weight fsaf_r50_fpn_1x_coco-94ccc51f.pth --cfg fsaf_r50_fpn_1x_coco.py --output fsaf.onnx
# use onnxsim optimize onnx model
onnxsim fsaf.onnx fsaf_opt.onnxexport DATASETS_DIR=/Path/to/coco/# Accuracy
bash scripts/infer_fsaf_fp16_accuracy.sh
# Performance
bash scripts/infer_fsaf_fp16_performance.sh| Model | BatchSize | Precision | FPS | IOU@0.5 | IOU@0.5:0.95 |
|---|---|---|---|---|---|
| FSAF | 32 | FP16 | 122.35 | 0.530 | 0.345 |