Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
paper address: https://arxiv.org/pdf/1912.02424.pdf
- Linux OS
- Python 3.6 or higher (Python 2 is not supported)
- PyTorch 1.2 or higher
- mmcv 0.4.3
- CUDA 10.0
- GCC(G++) 5.4.0 or higher
a. Create a conda virtual environment and activate it (Optional but recommended).
conda create --name atss python=3.6
conda activate atssb. Install pytorch and torchvision.
pip is recommended,
pip install torch==1.2.0 torchvision==0.4.0 # CUDA 10.0c. Install mmdet (other dependencies wil be installed automatically).
pip install -r requirements.txt
python setup.py build develop
pip install -v -e .d. Prepare dataset and checkpoint file.
Download coco dataset and checkpoint file
Fold structure should be as follows:
ATSS-Pytorch
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
├── backbone
│ ├── resnet50-19c8e357.pth
bash scripts/train.shbash scripts/test.sh| Backbone | Lr schd | box AP | AP50 | AP75 | AP_S | AP_M | AP_L | Download |
|---|---|---|---|---|---|---|---|---|
| R-50-FPN | 1x | 35.6 | 55.7 | 37.8 | 20.2 | 39.1 | 47.0 | - |
| R-50-FPN(ATSS) | 1x | 34.3 | 54.8 | 36.3 | 19.5 | 37.3 | 44.4 | - |
| R-50-FPN | 2x | - | ||||||
| R-50-FPN(ATSS) | 2x | - | ||||||
| R-50-FPN(A=1) | 1x | 30.1 | 49.4 | 31.3 | 14.6 | 33.8 | 40.7 | - |
| R-50-FPN(A=1,ATSS) | 1x | 36.0 | 56.1 | 38.1 | 20.0 | 39.6 | 46.9 | - |
| R-50-FPN(A=1) | 2x | - | ||||||
| R-50-FPN(A=1,ATSS) | 2x | - |
[1] 1x and 2x mean the model is trained for 90K and 180K iterations, respectively.
[2] (A=1) mean retinanet only has one 8S square anchor in each level (S is stride)