OTA: Optimal Transport Assignment for Object Detection

This project provides an implementation for our CVPR2021 paper "OTA: Optimal Transport Assignment for Object Detection" on PyTorch.

Requirements

• cvpods

Get Started

• install cvpods locally (requires cuda to compile)

python3 -m pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git'
# (add --user if you don't have permission)

# Or, to install it from a local clone:
git clone https://github.com/Megvii-BaseDetection/cvpods.git
python3 -m pip install -e cvpods

# Or,
pip install -r requirements.txt
python3 setup.py build develop

• prepare datasets

cd /path/to/cvpods/datasets
ln -s /path/to/your/coco/dataset coco

• Train & Test

git clone https://github.com/Megvii-BaseDetection/OTA.git
cd playground/detection/coco/ota.res50.fpn.coco.800size.1x  # for example

# Train
pods_train --num-gpus 8

# Test
pods_test --num-gpus 8 \
    MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
    OUTPUT_DIR /path/to/your/save_dir # optional

# Multi node training
## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"

Results on COCO val set

Results on COCO test-dev

* stands for ATSS-style testing time augmentation. To enable testing time augmentation, add/modify the following code frac in the corresponding config.py

TEST=dict(
    DETECTIONS_PER_IMAGE=300,
    AUG=dict(
        ENABLED=True,
        MAX_SIZE=3000,
        MIN_SIZES=(400, 500, 600, 640, 700, 900, 1000, 1100, 1200, 1300, 1400, 1800),
        EXTRA_SIZES=((800, 1333),),
        SCALE_FILTER=True,
        SCALE_RANGES=(
        [96, 10000], [96, 10000], [64, 10000], [64, 10000], [64, 10000], [0, 10000], [0, 10000], [0, 256], [0, 256], [0, 192], [0, 192], [0, 96], [0, 10000])
    )
),

Acknowledgement

This repo is developed based on cvpods. Please check cvpods for more details and features.

License

This repo is released under the Apache 2.0 license. Please see the LICENSE file for more information.