This is the official implementation of the paper:
Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu and Dong Huang, Multiple Anchor Learning for Visual Object Detection, CVPR, 2020, PDF
@inproceedings{kehuang2020,
title={Multiple Anchor Learning for Visual Object Detection},
author={Wei Ke and Tianliang Zhang and Zeyi Huang and Qixiang Ye and Jianzhuang Liu and Dong Huang},
booktitle={CVPR},
year={2020}
}This repo includes the basic training and inference pipeline based on maskrcnn_benckmark .
For MAL fast inference, please direct to MAL-inference.
For MAL detection&tracking extention, please direct to MAL-inference-deepsort.
- Python3
- PyTorch 1.1 with CUDA support
- torchvision 0.2.1
- pycocotools
- yacs
- matplotlib
- GCC >= 4.9
- (optional) OpenCV for the webcam demo
# first, make sure that your conda is setup properly with the right environment
# for that, check that `which conda`, `which pip` and `which python` points to the
# right path. From a clean conda env, this is what you need to do
conda create --name free_anchor python=3.7
conda activate free_anchor
# this installs the right pip and dependencies for the fresh python
conda install ipython
# maskrnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm
# pytorch and torchvision
# we give the instructions for CUDA 10.0
conda install pytorch=1.1 torchvision=0.2.1 cudatoolkit=10.0 -c pytorch
# install pycocotools
pip install pycocotools
# install MAL
git clone https://github.com/DeLightCMU/MAL.git
git checkout finetune
# the following will install the lib with
# symbolic links, so that you can modify
# the files if you want and won't need to
# re-build it
cd MAL
bash build_maskrcnn.shFor that, all you need to do is to modify maskrcnn_benchmark/config/paths_catalog.py to point to the location where your dataset is stored.
We provide the following MAL models pre-trained on COCO2017.
| Config File | Backbone | test-dev mAP (single-scale) | pth models |
|---|---|---|---|
| configs/mal_R-50-FPN | ResNet-50-FPN | 39.2 | download |
| configs/mal_R-101-FPN | ResNet-101-FPN | 43.6 | download |
| configs/mal_X-101-FPN | ResNext-101-FPN | 45.9 | download |
cd MAL
CUDA_VISIBLE_DEVICES=1,2,3,4 python -m torch.distributed.launch --nproc_per_node=4 tools/train_net.py --config-file ./configs/MAL_R-50-FPN_e2e.yaml SOLVER.IMS_PER_BATCH 4 MODEL.WEIGHT path_to_pretrained_modelcd MAL/tools
python transfer_to_coco_json_visdrone.py