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OTA: Optimal Transport Assignment for Object Detection

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

Requirements

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

Model Backbone LR Sched. mAP Recall AP50/AP75/APs/APm/APl Download
RetinaNet R50 1x 36.5 53.4 56.2/39.3/21.9/40.5/47.7 -
Faster R-CNN R50 1x 38.1 52.2 58.9/41.0/22.5/41.5/48.9 -
FCOS R50 1x 38.7 57.0 57.5/41.7/22.6/42.7/49.9 -
FreeAnchor R50 1x 38.4 55.4 57.0/41.1/21.9/41.7/51.8 -
ATSS R50 1x 39.4 57.7 57.5/42.7/22.9/42.9/51.2 -
PAA(w/. Voting) R50 1x 40.4 - - -
OTA R50 1x 40.7 59.0 58.4/44.3/23.2/45.0/53.6 weights

Results on COCO test-dev

Model Backbone LR Sched. Training Scale (ShortSide) mAP AP50/AP75/APs/APm/APl Download
OTA R101 2x 640~800 45.3 63.5/49.3/26.9/48.8/56.1 weights
OTA X101 2x 640~800 47.0 65.8/51.1/29.2/50.4/57.9 weights
OTA X101-DCN 2x 640~800 49.2 67.6/53.5/30.0/52.5/62.3 weights
OTA* X101-DCN 2x 640~800 51.5 68.6/57.1/34.1/53.7/64.1 weights

* 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.

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Official implementation of our CVPR2021 paper "OTA: Optimal Transport Assignment for Object Detection" in Pytorch.

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