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4 年前
4 年前
4 年前
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  1. <a href="https://apps.apple.com/app/id1452689527" target="_blank">
  2. <img src="https://user-images.githubusercontent.com/26833433/82944393-f7644d80-9f4f-11ea-8b87-1a5b04f555f1.jpg" width="1000"></a>
  3. &nbsp
  4. ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
  5. This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
  6. <img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  7. - **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
  8. - **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972).
  9. - **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
  10. - **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  11. - **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
  12. - **April 1, 2020**: Start development of future compound-scaled [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models.
  13. ## Pretrained Checkpoints
  14. | Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
  15. |---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
  16. | [YOLOv5s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 36.1 | 36.1 | 55.3 | **2.1ms** | **476** || 7.5M | 13.2B
  17. | [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 43.5 | 43.5 | 62.5 | 3.0ms | 333 || 21.8M | 39.4B
  18. | [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 47.0 | 47.1 | 65.6 | 3.9ms | 256 || 47.8M | 88.1B
  19. | [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | **49.0** | **49.0** | **67.4** | 6.1ms | 164 || 89.0M | 166.4B
  20. | | | | | | || |
  21. | [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B
  22. ** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
  23. ** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 672 --conf 0.001`
  24. ** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1`
  25. ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
  26. ## Requirements
  27. Python 3.7 or later with all `requirements.txt` dependencies installed, including `torch >= 1.5`. To install run:
  28. ```bash
  29. $ pip install -U -r requirements.txt
  30. ```
  31. ## Tutorials
  32. * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)
  33. * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
  34. * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
  35. * [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
  36. * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
  37. * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
  38. * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
  39. ## Environments
  40. YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
  41. - **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
  42. - **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)
  43. - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
  44. - **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)
  45. ## Inference
  46. Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`.
  47. ```bash
  48. $ python detect.py --source 0 # webcam
  49. file.jpg # image
  50. file.mp4 # video
  51. path/ # directory
  52. path/*.jpg # glob
  53. rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
  54. http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
  55. ```
  56. To run inference on examples in the `./inference/images` folder:
  57. ```bash
  58. $ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
  59. Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
  60. Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
  61. Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
  62. image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
  63. image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
  64. Results saved to /content/yolov5/inference/output
  65. ```
  66. <img src="https://user-images.githubusercontent.com/26833433/83082816-59e54880-a039-11ea-8abe-ab90cc1ec4b0.jpeg" width="500">
  67. ## Training
  68. Download [COCO](https://github.com/ultralytics/yolov5/blob/master/data/get_coco2017.sh), install [Apex](https://github.com/NVIDIA/apex) and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
  69. ```bash
  70. $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
  71. yolov5m 48
  72. yolov5l 32
  73. yolov5x 16
  74. ```
  75. <img src="https://user-images.githubusercontent.com/26833433/84186698-c4d54d00-aa45-11ea-9bde-c632c1230ccd.png" width="900">
  76. ## Citation
  77. [![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686)
  78. ## About Us
  79. Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  80. - **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.**
  81. - **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
  82. - **Custom data training**, hyperparameter evolution, and model exportation to any destination.
  83. For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
  84. ## Contact
  85. **Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.