40 lines
1.2 KiB
Markdown
40 lines
1.2 KiB
Markdown
# DETR
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## Introduction
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DETR is an object detection model based on transformer. 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 | DETR | 4 | --- | 42.3 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/detr/detr_r50_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/detr_r50_1x_coco.pdparams) |
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**Notes:**
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- DETR is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.
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- DETR uses 8GPU to train 500 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/detr/detr_r50_1x_coco.yml --fleet
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```
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## Citations
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```
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@inproceedings{detr,
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author = {Nicolas Carion and
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Francisco Massa and
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Gabriel Synnaeve and
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Nicolas Usunier and
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Alexander Kirillov and
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Sergey Zagoruyko},
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title = {End-to-End Object Detection with Transformers},
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booktitle = {ECCV},
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year = {2020}
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}
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```
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