AutoAssign is an anchor-free object detection model that introduces a fully differentiable label assignment mechanism. It combines Center Weighting and Confidence Weighting to adaptively determine positive and negative samples during training. Center Weighting adjusts category-specific prior distributions, while Confidence Weighting customizes assignment strategies for each instance. This approach eliminates the need for manual anchor design and achieves appearance-aware detection through automatic sample selection, resulting in improved performance and reduced human intervention in the detection process.
| GPU | IXUCA SDK | Release |
|---|---|---|
| BI-V150 | 4.2.0 | 25.03 |
| BI-V100 | 2.2.0 | 22.09 |
mkdir -p data
ln -s /path/to/coco/ ./data
# Prepare resnet50_msra-5891d200.pth, skip this if fast network
mkdir -p /root/.cache/torch/hub/checkpoints/
wget https://download.openmmlab.com/pretrain/third_party/resnet50_msra-5891d200.pth -O /root/.cache/torch/hub/checkpoints/resnet50_msra-5891d200.pthGo to visit COCO official website, then select the COCO dataset you want to download.
Take coco2017 dataset as an example, specify /path/to/coco2017 to your COCO path in later training process, the
unzipped dataset path structure sholud look like:
coco2017
├── annotations
│ ├── instances_train2017.json
│ ├── instances_val2017.json
│ └── ...
├── train2017
│ ├── 000000000009.jpg
│ ├── 000000000025.jpg
│ └── ...
├── val2017
│ ├── 000000000139.jpg
│ ├── 000000000285.jpg
│ └── ...
├── train2017.txt
├── val2017.txt
└── ...# Install libGL
## CentOS
yum install -y mesa-libGL
## Ubuntu
apt install -y libgl1-mesa-glx
# install MMDetection
git clone https://github.com/open-mmlab/mmdetection.git -b v3.3.0 --depth=1
cd mmdetection
pip install -v -e .# One single GPU
python3 tools/train.py configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py
# Multiple GPUs on one machine
sed -i 's/python /python3 /g' tools/dist_train.sh
bash tools/dist_train.sh configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py 8