36 lines
1.2 KiB
Markdown
36 lines
1.2 KiB
Markdown
# TOOD
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## Introduction
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[TOOD: Task-aligned One-stage Object Detection](https://arxiv.org/abs/2108.07755)
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TOOD is an object detection model. We reproduced the model of the paper.
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## Model Zoo
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| Backbone | Model | Images/GPU | Inf time (fps) | Box AP | Config | Download |
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|:------:|:--------:|:--------:|:--------------:|:------:|:------:|:--------:|
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| R-50 | TOOD | 4 | --- | 42.5 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/tood/tood_r50_fpn_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/tood_r50_fpn_1x_coco.pdparams) |
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**Notes:**
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- TOOD is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.
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- TOOD uses 8GPU to train 12 epochs.
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GPU multi-card training
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```bash
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export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/tood/tood_r50_fpn_1x_coco.yml --fleet
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```
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## Citations
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```
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@inproceedings{feng2021tood,
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title={TOOD: Task-aligned One-stage Object Detection},
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author={Feng, Chengjian and Zhong, Yujie and Gao, Yu and Scott, Matthew R and Huang, Weilin},
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booktitle={ICCV},
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year={2021}
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}
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```
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