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Deformable-DETR-main.zip
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上传者:m0_51665349
更新日期:2025-09-22

Deformable-DETR模型代码

资源文件列表(大概)

文件名
大小
Deformable-DETR-main/
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Deformable-DETR-main/LICENSE
11.68KB
Deformable-DETR-main/README.md
10.27KB
Deformable-DETR-main/benchmark.py
2.39KB
Deformable-DETR-main/configs/
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Deformable-DETR-main/configs/r50_deformable_detr.sh
143B
Deformable-DETR-main/configs/r50_deformable_detr_plus_iterative_bbox_refinement.sh
198B
Deformable-DETR-main/configs/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh
236B
Deformable-DETR-main/configs/r50_deformable_detr_single_scale.sh
185B
Deformable-DETR-main/configs/r50_deformable_detr_single_scale_dc5.sh
206B
Deformable-DETR-main/datasets/
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Deformable-DETR-main/datasets/__init__.py
1.31KB
Deformable-DETR-main/datasets/coco.py
5.88KB
Deformable-DETR-main/datasets/coco_eval.py
8.96KB
Deformable-DETR-main/datasets/coco_panoptic.py
4.06KB
Deformable-DETR-main/datasets/data_prefetcher.py
3.01KB
Deformable-DETR-main/datasets/panoptic_eval.py
1.88KB
Deformable-DETR-main/datasets/samplers.py
5.48KB
Deformable-DETR-main/datasets/torchvision_datasets/
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Deformable-DETR-main/datasets/torchvision_datasets/__init__.py
329B
Deformable-DETR-main/datasets/torchvision_datasets/coco.py
3.21KB
Deformable-DETR-main/datasets/transforms.py
8.75KB
Deformable-DETR-main/docs/
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Deformable-DETR-main/docs/changelog.md
383B
Deformable-DETR-main/engine.py
7.33KB
Deformable-DETR-main/figs/
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Deformable-DETR-main/figs/convergence.png
53.36KB
Deformable-DETR-main/figs/illustration.png
265.79KB
Deformable-DETR-main/main.py
15.18KB
Deformable-DETR-main/models/
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Deformable-DETR-main/models/__init__.py
591B
Deformable-DETR-main/models/backbone.py
5.17KB
Deformable-DETR-main/models/deformable_detr.py
22.82KB
Deformable-DETR-main/models/deformable_transformer.py
21.98KB
Deformable-DETR-main/models/matcher.py
4.85KB
Deformable-DETR-main/models/ops/
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Deformable-DETR-main/models/ops/functions/
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Deformable-DETR-main/models/ops/functions/__init__.py
598B
Deformable-DETR-main/models/ops/functions/ms_deform_attn_func.py
3.22KB
Deformable-DETR-main/models/ops/make.sh
593B
Deformable-DETR-main/models/ops/modules/
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Deformable-DETR-main/models/ops/modules/__init__.py
584B
Deformable-DETR-main/models/ops/modules/ms_deform_attn.py
6.12KB
Deformable-DETR-main/models/ops/setup.py
2.5KB
Deformable-DETR-main/models/ops/src/
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Deformable-DETR-main/models/ops/src/cpu/
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Deformable-DETR-main/models/ops/src/cpu/ms_deform_attn_cpu.cpp
1.23KB
Deformable-DETR-main/models/ops/src/cpu/ms_deform_attn_cpu.h
1.11KB
Deformable-DETR-main/models/ops/src/cuda/
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Deformable-DETR-main/models/ops/src/cuda/ms_deform_attn_cuda.cu
7.14KB
Deformable-DETR-main/models/ops/src/cuda/ms_deform_attn_cuda.h
1.11KB
Deformable-DETR-main/models/ops/src/cuda/ms_deform_im2col_cuda.cuh
53.41KB
Deformable-DETR-main/models/ops/src/ms_deform_attn.h
1.79KB
Deformable-DETR-main/models/ops/src/vision.cpp
799B
Deformable-DETR-main/models/ops/test.py
3.99KB
Deformable-DETR-main/models/position_encoding.py
3.7KB
Deformable-DETR-main/models/segmentation.py
15.55KB
Deformable-DETR-main/requirements.txt
30B
Deformable-DETR-main/tools/
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Deformable-DETR-main/tools/launch.py
9.09KB
Deformable-DETR-main/tools/run_dist_launch.sh
812B
Deformable-DETR-main/tools/run_dist_slurm.sh
1.13KB
Deformable-DETR-main/util/
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Deformable-DETR-main/util/__init__.py
506B
Deformable-DETR-main/util/box_ops.py
2.93KB
Deformable-DETR-main/util/misc.py
17.16KB
Deformable-DETR-main/util/plot_utils.py
4.55KB
Deformable-DETR-main/.idea/
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Deformable-DETR-main/.idea/.gitignore
50B
Deformable-DETR-main/.idea/modules.xml
299B
Deformable-DETR-main/.idea/inspectionProfiles/
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Deformable-DETR-main/.idea/inspectionProfiles/profiles_settings.xml
174B
Deformable-DETR-main/.idea/vcs.xml
185B
Deformable-DETR-main/.idea/Deformable-DETR-main.iml
492B
Deformable-DETR-main/.idea/misc.xml
195B
Deformable-DETR-main/.idea/workspace.xml
2.41KB
Deformable-DETR-main/venv/
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Deformable-DETR-main/venv/Lib/
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Deformable-DETR-main/venv/Lib/site-packages/
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Deformable-DETR-main/venv/Scripts/
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Deformable-DETR-main/venv/Scripts/python.exe
89.5KB
Deformable-DETR-main/venv/Scripts/pythonw.exe
89.5KB
Deformable-DETR-main/venv/pyvenv.cfg
327B
Deformable-DETR-main/.git/
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Deformable-DETR-main/.git/description
73B
Deformable-DETR-main/.git/hooks/
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Deformable-DETR-main/.git/hooks/applypatch-msg.sample
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Deformable-DETR-main/.git/hooks/commit-msg.sample
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Deformable-DETR-main/.git/hooks/fsmonitor-watchman.sample
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Deformable-DETR-main/.git/hooks/post-update.sample
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Deformable-DETR-main/.git/hooks/pre-applypatch.sample
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Deformable-DETR-main/.git/hooks/pre-commit.sample
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Deformable-DETR-main/.git/hooks/pre-rebase.sample
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Deformable-DETR-main/.git/hooks/pre-receive.sample
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Deformable-DETR-main/.git/hooks/prepare-commit-msg.sample
1.46KB
Deformable-DETR-main/.git/hooks/update.sample
3.53KB
Deformable-DETR-main/.git/info/
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Deformable-DETR-main/.git/info/exclude
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Deformable-DETR-main/.git/refs/
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Deformable-DETR-main/.git/refs/heads/
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Deformable-DETR-main/.git/refs/heads/master
41B
Deformable-DETR-main/.git/refs/heads/CCFM-Deformable-DETR
41B
Deformable-DETR-main/.git/refs/tags/
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Deformable-DETR-main/.git/refs/remotes/origin/
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Deformable-DETR-main/.git/refs/remotes/origin/master
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资源内容介绍

Deformable-DETR模型代码
# Deformable DETRBy [Xizhou Zhu](https://scholar.google.com/citations?user=02RXI00AAAAJ), [Weijie Su](https://www.weijiesu.com/), [Lewei Lu](https://www.linkedin.com/in/lewei-lu-94015977/), [Bin Li](http://staff.ustc.edu.cn/~binli/), [Xiaogang Wang](http://www.ee.cuhk.edu.hk/~xgwang/), [Jifeng Dai](https://jifengdai.org/).This repository is an official implementation of the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159).## Introduction**TL; DR.** Deformable DETR is an efficient and fast-converging end-to-end object detector. It mitigates the high complexity and slow convergence issues of DETR via a novel sampling-based efficient attention mechanism. ![deformable_detr](./figs/illustration.png)![deformable_detr](./figs/convergence.png)**Abstract.** DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10× less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach.## LicenseThis project is released under the [Apache 2.0 license](./LICENSE).## ChangelogSee [changelog.md](./docs/changelog.md) for detailed logs of major changes. ## Citing Deformable DETRIf you find Deformable DETR useful in your research, please consider citing:```bibtex@article{zhu2020deformable, title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, journal={arXiv preprint arXiv:2010.04159}, year={2020}}```## Main Results| <sub><sub>Method</sub></sub> | <sub><sub>Epochs</sub></sub> | <sub><sub>AP</sub></sub> | <sub><sub>AP<sub>S</sub></sub></sub> | <sub><sub>AP<sub>M</sub></sub></sub> | <sub><sub>AP<sub>L</sub></sub></sub> | <sub><sub>params<br>(M)</sub></sub> | <sub><sub>FLOPs<br>(G)</sub></sub> | <sub><sub>Total<br>Train<br>Time<br>(GPU<br/>hours)</sub></sub> | <sub><sub>Train<br/>Speed<br>(GPU<br/>hours<br/>/epoch)</sub></sub> | <sub><sub>Infer<br/>Speed<br/>(FPS)</sub></sub> | <sub><sub>Batch<br/>Infer<br/>Speed<br>(FPS)</sub></sub> | <sub><sub>URL</sub></sub> || ----------------------------------- | :----: | :--: | :----: | :---: | :------------------------------: | :--------------------:| :----------------------------------------------------------: | :--: | :---: | :---: | ----- | ----- || <sub><sub>Faster R-CNN + FPN</sub></sub> | <sub>109</sub> | <sub>42.0</sub> | <sub>26.6</sub> | <sub>45.4</sub> | <sub>53.4</sub> | <sub>42</sub> | <sub>180</sub> | <sub>380</sub> | <sub>3.5</sub> | <sub>25.6</sub> | <sub>28.0</sub> | <sub>-</sub> || <sub><sub>DETR</sub></sub> | <sub>500</sub> | <sub>42.0</sub> | <sub>20.5</sub> | <sub>45.8</sub> | <sub>61.1</sub> | <sub>41</sub> | <sub>86</sub> | <sub>2000</sub> | <sub>4.0</sub> | <sub>27.0</sub> | <sub>38.3</sub> | <sub>-</sub> || <sub><sub>DETR-DC5</sub></sub> | <sub>500</sub> | <sub>43.3</sub> | <sub>22.5</sub> | <sub>47.3</sub> | <sub>61.1</sub> | <sub>41</sub> |<sub>187</sub>|<sub>7000</sub>|<sub>14.0</sub>|<sub>11.4</sub>|<sub>12.4</sub>| <sub>-</sub> || <sub><sub>DETR-DC5</sub></sub> | <sub>50</sub> | <sub>35.3</sub> | <sub>15.2</sub> | <sub>37.5</sub> | <sub>53.6</sub> | <sub>41</sub> |<sub>187</sub>|<sub>700</sub>|<sub>14.0</sub>|<sub>11.4</sub>|<sub>12.4</sub>| <sub>-</sub> || <sub><sub>DETR-DC5+</sub></sub> | <sub>50</sub> | <sub>36.2</sub> | <sub>16.3</sub> | <sub>39.2</sub> | <sub>53.9</sub> | <sub>41</sub> |<sub>187</sub>|<sub>700</sub>|<sub>14.0</sub>|<sub>11.4</sub>|<sub>12.4</sub>| <sub>-</sub> || **<sub><sub>Deformable DETR<br>(single scale)</sub></sub>** | <sub>50</sub> | <sub>39.4</sub> | <sub>20.6</sub> | <sub>43.0</sub> | <sub>55.5</sub> | <sub>34</sub> |<sub>78</sub>|<sub>160</sub>|<sub>3.2</sub>|<sub>27.0</sub>|<sub>42.4</sub>| <sub>[config](./configs/r50_deformable_detr_single_scale.sh)<br/>[log](https://drive.google.com/file/d/1n3ZnZ-UAqmTUR4AZoM4qQntIDn6qCZx4/view?usp=sharing)<br/>[model](https://drive.google.com/file/d/1WEjQ9_FgfI5sw5OZZ4ix-OKk-IJ_-SDU/view?usp=sharing)</sub> || **<sub><sub>Deformable DETR<br>(single scale, DC5)</sub></sub>** | <sub>50</sub> | <sub>41.5</sub> | <sub>24.1</sub> | <sub>45.3</sub> | <sub>56.0</sub> | <sub>34</sub> |<sub>128</sub>|<sub>215</sub>|<sub>4.3</sub>|<sub>22.1</sub>|<sub>29.4</sub>| <sub>[config](./configs/r50_deformable_detr_single_scale_dc5.sh)<br/>[log](https://drive.google.com/file/d/1-UfTp2q4GIkJjsaMRIkQxa5k5vn8_n-B/view?usp=sharing)<br/>[model](https://drive.google.com/file/d/1m_TgMjzH7D44fbA-c_jiBZ-xf-odxGdk/view?usp=sharing)</sub> || **<sub><sub>Deformable DETR</sub></sub>** | <sub>50</sub> | <sub>44.5</sub> | <sub>27.1</sub> | <sub>47.6</sub> | <sub>59.6</sub> | <sub>40</sub> |<sub>173</sub>|<sub>325</sub>|<sub>6.5</sub>|<sub>15.0</sub>|<sub>19.4</sub>|<sub>[config](./configs/r50_deformable_detr.sh)<br/>[log](https://drive.google.com/file/d/18YSLshFjc_erOLfFC-hHu4MX4iyz1Dqr/view?usp=sharing)<br/>[model](https://drive.google.com/file/d/1nDWZWHuRwtwGden77NLM9JoWe-YisJnA/view?usp=sharing)</sub> || **<sub><sub>+ iterative bounding box refinement</sub></sub>** | <sub>50</sub> | <sub>46.2</sub> | <sub>28.3</sub> | <sub>49.2</sub> | <sub>61.5</sub> | <sub>41</sub> |<sub>173</sub>|<sub>325</sub>|<sub>6.5</sub>|<sub>15.0</sub>|<sub>19.4</sub>|<sub>[config](./configs/r50_deformable_detr_plus_iterative_bbox_refinement.sh)<br/>[log](https://drive.google.com/file/d/1DFNloITi1SFBWjYzvVEAI75ndwmGM1Uj/view?usp=sharing)<br/>[model](https://drive.google.com/file/d/1JYKyRYzUH7uo9eVfDaVCiaIGZb5YTCuI/view?usp=sharing)</sub> || **<sub><sub>++ two-stage Deformable DETR</sub></sub>** | <sub>50</sub> | <sub>46.9</sub> | <sub>29.6</sub> | <sub>50.1</sub> | <sub>61.6</sub> | <sub>41</sub> |<sub>173</sub>|<sub>340</sub>|<sub>6.8</sub>|<sub>14.5</sub>|<sub>18.8</sub>|<sub>[config](./configs/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh)<br/>[log](https://drive.google.com/file/d/1ozi0wbv5-Sc5TbWt1jAuXco72vEfEtbY/view?usp=sharing) <br/>[model](https://drive.google.com/file/d/15I03A7hNTpwuLNdfuEmW9_taZMNVssEp/view?usp=sharing)</sub> |*Note:*1. All models of Deformable DETR are trained with total batch size of 32. 2. Training and inference speed are measured on NVIDIA Tesla V100 GPU.3. "Deformable DETR (single scale)" means only using res5 feature map (of stride 32) as input feature maps for Deformable Transformer Encoder.4. "DC5" means removing the stride in C5 stage of ResNet and add a dilation of 2 instead.5. "DETR-DC5+" indicates DETR-DC5 with some modifications, including using Focal Loss for bounding box classification and increasing number of object queries to 300.6. "Batch Infer Speed" refer to inference with batch size = 4 to maximize GPU utilization.7. The original implementation is based on our internal codebase. There are slight differences in the final accuracy and running time due to the plenty details in platform switch.## Installation### Requirements* Linux, CUDA>=9.2, GCC>=5.4 * Python>=3.7 We recommend you to use Anaconda to create a conda environment: ```bash conda create -n deformable_detr python=3.7 pip ``` Then, activate the environment: ```bash conda activate deformable_detr ``` * PyTorch>=1.5.1, torchvision>=0.6.1 (following instructions [here](https://pytorch.org/)) For example, if your CUDA version is 9.2, you coul

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