首页下载资源人工智能Deformable-DETR模型代码

ZIPDeformable-DETR模型代码

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资源文件列表:

Deformable-DETR-main.zip 大约有308个文件
  1. Deformable-DETR-main/
  2. Deformable-DETR-main/LICENSE 11.68KB
  3. Deformable-DETR-main/README.md 10.27KB
  4. Deformable-DETR-main/benchmark.py 2.39KB
  5. Deformable-DETR-main/configs/
  6. Deformable-DETR-main/configs/r50_deformable_detr.sh 143B
  7. Deformable-DETR-main/configs/r50_deformable_detr_plus_iterative_bbox_refinement.sh 198B
  8. Deformable-DETR-main/configs/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh 236B
  9. Deformable-DETR-main/configs/r50_deformable_detr_single_scale.sh 185B
  10. Deformable-DETR-main/configs/r50_deformable_detr_single_scale_dc5.sh 206B
  11. Deformable-DETR-main/datasets/
  12. Deformable-DETR-main/datasets/__init__.py 1.31KB
  13. Deformable-DETR-main/datasets/coco.py 5.88KB
  14. Deformable-DETR-main/datasets/coco_eval.py 8.96KB
  15. Deformable-DETR-main/datasets/coco_panoptic.py 4.06KB
  16. Deformable-DETR-main/datasets/data_prefetcher.py 3.01KB
  17. Deformable-DETR-main/datasets/panoptic_eval.py 1.88KB
  18. Deformable-DETR-main/datasets/samplers.py 5.48KB
  19. Deformable-DETR-main/datasets/torchvision_datasets/
  20. Deformable-DETR-main/datasets/torchvision_datasets/__init__.py 329B
  21. Deformable-DETR-main/datasets/torchvision_datasets/coco.py 3.21KB
  22. Deformable-DETR-main/datasets/transforms.py 8.75KB
  23. Deformable-DETR-main/docs/
  24. Deformable-DETR-main/docs/changelog.md 383B
  25. Deformable-DETR-main/engine.py 7.33KB
  26. Deformable-DETR-main/figs/
  27. Deformable-DETR-main/figs/convergence.png 53.36KB
  28. Deformable-DETR-main/figs/illustration.png 265.79KB
  29. Deformable-DETR-main/main.py 15.18KB
  30. Deformable-DETR-main/models/
  31. Deformable-DETR-main/models/__init__.py 591B
  32. Deformable-DETR-main/models/backbone.py 5.17KB
  33. Deformable-DETR-main/models/deformable_detr.py 22.82KB
  34. Deformable-DETR-main/models/deformable_transformer.py 21.98KB
  35. Deformable-DETR-main/models/matcher.py 4.85KB
  36. Deformable-DETR-main/models/ops/
  37. Deformable-DETR-main/models/ops/functions/
  38. Deformable-DETR-main/models/ops/functions/__init__.py 598B
  39. Deformable-DETR-main/models/ops/functions/ms_deform_attn_func.py 3.22KB
  40. Deformable-DETR-main/models/ops/make.sh 593B
  41. Deformable-DETR-main/models/ops/modules/
  42. Deformable-DETR-main/models/ops/modules/__init__.py 584B
  43. Deformable-DETR-main/models/ops/modules/ms_deform_attn.py 6.12KB
  44. Deformable-DETR-main/models/ops/setup.py 2.5KB
  45. Deformable-DETR-main/models/ops/src/
  46. Deformable-DETR-main/models/ops/src/cpu/
  47. Deformable-DETR-main/models/ops/src/cpu/ms_deform_attn_cpu.cpp 1.23KB
  48. Deformable-DETR-main/models/ops/src/cpu/ms_deform_attn_cpu.h 1.11KB
  49. Deformable-DETR-main/models/ops/src/cuda/
  50. Deformable-DETR-main/models/ops/src/cuda/ms_deform_attn_cuda.cu 7.14KB
  51. Deformable-DETR-main/models/ops/src/cuda/ms_deform_attn_cuda.h 1.11KB
  52. Deformable-DETR-main/models/ops/src/cuda/ms_deform_im2col_cuda.cuh 53.41KB
  53. Deformable-DETR-main/models/ops/src/ms_deform_attn.h 1.79KB
  54. Deformable-DETR-main/models/ops/src/vision.cpp 799B
  55. Deformable-DETR-main/models/ops/test.py 3.99KB
  56. Deformable-DETR-main/models/position_encoding.py 3.7KB
  57. Deformable-DETR-main/models/segmentation.py 15.55KB
  58. Deformable-DETR-main/requirements.txt 30B
  59. Deformable-DETR-main/tools/
  60. Deformable-DETR-main/tools/launch.py 9.09KB
  61. Deformable-DETR-main/tools/run_dist_launch.sh 812B
  62. Deformable-DETR-main/tools/run_dist_slurm.sh 1.13KB
  63. Deformable-DETR-main/util/
  64. Deformable-DETR-main/util/__init__.py 506B
  65. Deformable-DETR-main/util/box_ops.py 2.93KB
  66. Deformable-DETR-main/util/misc.py 17.16KB
  67. Deformable-DETR-main/util/plot_utils.py 4.55KB
  68. Deformable-DETR-main/.idea/
  69. Deformable-DETR-main/.idea/.gitignore 50B
  70. Deformable-DETR-main/.idea/modules.xml 299B
  71. Deformable-DETR-main/.idea/inspectionProfiles/
  72. Deformable-DETR-main/.idea/inspectionProfiles/profiles_settings.xml 174B
  73. Deformable-DETR-main/.idea/vcs.xml 185B
  74. Deformable-DETR-main/.idea/Deformable-DETR-main.iml 492B
  75. Deformable-DETR-main/.idea/misc.xml 195B
  76. Deformable-DETR-main/.idea/workspace.xml 2.41KB
  77. Deformable-DETR-main/venv/
  78. Deformable-DETR-main/venv/Lib/
  79. Deformable-DETR-main/venv/Lib/site-packages/
  80. Deformable-DETR-main/venv/Scripts/
  81. Deformable-DETR-main/venv/Scripts/python.exe 89.5KB
  82. Deformable-DETR-main/venv/Scripts/pythonw.exe 89.5KB
  83. Deformable-DETR-main/venv/pyvenv.cfg 327B
  84. Deformable-DETR-main/.git/
  85. Deformable-DETR-main/.git/description 73B
  86. Deformable-DETR-main/.git/hooks/
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  98. Deformable-DETR-main/.git/info/
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  100. Deformable-DETR-main/.git/refs/
  101. Deformable-DETR-main/.git/refs/heads/
  102. Deformable-DETR-main/.git/refs/heads/master 41B
  103. Deformable-DETR-main/.git/refs/heads/CCFM-Deformable-DETR 41B
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资源介绍:

Deformable-DETR模型代码
# Deformable DETR By [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. ## License This project is released under the [Apache 2.0 license](./LICENSE). ## Changelog See [changelog.md](./docs/changelog.md) for detailed logs of major changes. ## Citing Deformable DETR If 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 | Method | Epochs | AP | APS | APM | APL | params
(M) | FLOPs
(G) | Total
Train
Time
(GPU
hours) | Train
Speed
(GPU
hours
/epoch) | Infer
Speed
(FPS) | Batch
Infer
Speed
(FPS) | URL | | ----------------------------------- | :----: | :--: | :----: | :---: | :------------------------------: | :--------------------:| :----------------------------------------------------------: | :--: | :---: | :---: | ----- | ----- | | Faster R-CNN + FPN | 109 | 42.0 | 26.6 | 45.4 | 53.4 | 42 | 180 | 380 | 3.5 | 25.6 | 28.0 | - | | DETR | 500 | 42.0 | 20.5 | 45.8 | 61.1 | 41 | 86 | 2000 | 4.0 | 27.0 | 38.3 | - | | DETR-DC5 | 500 | 43.3 | 22.5 | 47.3 | 61.1 | 41 |187|7000|14.0|11.4|12.4| - | | DETR-DC5 | 50 | 35.3 | 15.2 | 37.5 | 53.6 | 41 |187|700|14.0|11.4|12.4| - | | DETR-DC5+ | 50 | 36.2 | 16.3 | 39.2 | 53.9 | 41 |187|700|14.0|11.4|12.4| - | | **Deformable DETR
(single scale)** | 50 | 39.4 | 20.6 | 43.0 | 55.5 | 34 |78|160|3.2|27.0|42.4| [config](./configs/r50_deformable_detr_single_scale.sh)
[log](https://drive.google.com/file/d/1n3ZnZ-UAqmTUR4AZoM4qQntIDn6qCZx4/view?usp=sharing)
[model](https://drive.google.com/file/d/1WEjQ9_FgfI5sw5OZZ4ix-OKk-IJ_-SDU/view?usp=sharing) | | **Deformable DETR
(single scale, DC5)** | 50 | 41.5 | 24.1 | 45.3 | 56.0 | 34 |128|215|4.3|22.1|29.4| [config](./configs/r50_deformable_detr_single_scale_dc5.sh)
[log](https://drive.google.com/file/d/1-UfTp2q4GIkJjsaMRIkQxa5k5vn8_n-B/view?usp=sharing)
[model](https://drive.google.com/file/d/1m_TgMjzH7D44fbA-c_jiBZ-xf-odxGdk/view?usp=sharing) | | **Deformable DETR** | 50 | 44.5 | 27.1 | 47.6 | 59.6 | 40 |173|325|6.5|15.0|19.4|[config](./configs/r50_deformable_detr.sh)
[log](https://drive.google.com/file/d/18YSLshFjc_erOLfFC-hHu4MX4iyz1Dqr/view?usp=sharing)
[model](https://drive.google.com/file/d/1nDWZWHuRwtwGden77NLM9JoWe-YisJnA/view?usp=sharing) | | **+ iterative bounding box refinement** | 50 | 46.2 | 28.3 | 49.2 | 61.5 | 41 |173|325|6.5|15.0|19.4|[config](./configs/r50_deformable_detr_plus_iterative_bbox_refinement.sh)
[log](https://drive.google.com/file/d/1DFNloITi1SFBWjYzvVEAI75ndwmGM1Uj/view?usp=sharing)
[model](https://drive.google.com/file/d/1JYKyRYzUH7uo9eVfDaVCiaIGZb5YTCuI/view?usp=sharing) | | **++ two-stage Deformable DETR** | 50 | 46.9 | 29.6 | 50.1 | 61.6 | 41 |173|340|6.8|14.5|18.8|[config](./configs/r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage.sh)
[log](https://drive.google.com/file/d/1ozi0wbv5-Sc5TbWt1jAuXco72vEfEtbY/view?usp=sharing)
[model](https://drive.google.com/file/d/15I03A7hNTpwuLNdfuEmW9_taZMNVssEp/view?usp=sharing) | *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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