iou-enhanced-attention's Introduction

IoU-Enhanced-Attention

IoU-Enhanced Attention for End-to-End Task Specific Object Detection

Paper

IoU-Enhanced Attention for End-to-End Task Specific Object Detection

Models

Method	box AP	download
R50_100pro_3x	44.4	model
R50_300pro_3x	46.4	model
R101_100pro_3x	45.6	model
R101_300pro_3x	47.5	model

Models are available in Baidu Drive by code jysg.

Installation

The codebases are built on top of Detectron2 and SparseR-CNN.

Requirements

Linux or macOS with Python ≥ 3.6
PyTorch ≥ 1.5 and torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this
OpenCV is optional and needed by demo and visualization

Steps

Install and build libs

git clone https://github.com/bravezzzzzz/IoU-Enhanced-Attention.git
cd IoU-Enhanced-Attention
python setup.py build develop

Link coco dataset path to IoU-Enhanced-Attention/datasets/coco

mkdir -p datasets/coco
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017

Train

python projects/SparseRCNN/train_net.py --num-gpus 8 \
    --config-file projects/SparseRCNN/configs/sparsercnn.res50.100pro.3x.yaml

Evaluate

python projects/SparseRCNN/train_net.py --num-gpus 8 \
    --config-file projects/SparseRCNN/configs/sparsercnn.res50.100pro.3x.yaml \
    --eval-only MODEL.WEIGHTS path/to/model.pth

Visualize

python demo/demo.py\
    --config-file projects/SparseRCNN/configs/sparsercnn.res50.100pro.3x.yaml \
    --input path/to/images --output path/to/save_images --confidence-threshold 0.4 \
    --opts MODEL.WEIGHTS path/to/model.pth

Citing

If you use this code for your research, please cite

@article{zhao2022iou,
  title={IoU-Enhanced Attention for End-to-End Task Specific Object Detection},
  author={Zhao, Jing and Wu, Shengjian and Sun, Li and Li, Qingli},
  journal={arXiv preprint arXiv:2209.10391},
  year={2022}
}

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