yolov5: yolov5官方源码

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yolov5: yolov5官方源码

2023-10-05 20:03| 来源: 网络整理| 查看: 265

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

Documentation

See the YOLOv5 Docs for full documentation on training, testing and deployment.

Quick Start Examples Install

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/ultralytics/yolov5 $ cd yolov5 $ pip install -r requirements.txt Inference

Inference with YOLOv5 and PyTorch Hub. Models automatically download from the latest YOLOv5 release.

import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom # Images img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/NUsoVlDFqZg' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Training

Run commands below to reproduce results on COCO dataset (dataset auto-downloads on first use). 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).

$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 yolov5m 40 yolov5l 24 yolov5x 16

Tutorials Train Custom Data 🚀 RECOMMENDED Tips for Best Training Results ☘️ RECOMMENDED Weights & Biases Logging 🌟 NEW Roboflow for Datasets, Labeling, and Active Learning 🌟 NEW Multi-GPU Training PyTorch Hub ⭐ NEW TorchScript, ONNX, CoreML Export 🚀 Test-Time Augmentation (TTA) Model Ensembling Model Pruning/Sparsity Hyperparameter Evolution Transfer Learning with Frozen Layers ⭐ NEW TensorRT Deployment Environments

Get started in seconds with our verified environments. Click each icon below for details.

Integrations

Weights and Biases Roboflow ⭐ NEW Automatically track and visualize all your YOLOv5 training runs in the cloud with Weights & Biases Label and automatically export your custom datasets directly to YOLOv5 for training with Roboflow Why YOLOv5

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand) COCO AP val denotes [email protected]:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536. GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32. EfficientDet data from google/automl at batch size 8. Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt Pretrained Checkpoints Model size(pixels) mAPval0.5:0.95 mAPval0.5 SpeedCPU b1(ms) SpeedV100 b1(ms) SpeedV100 b32(ms) params(M) FLOPs@640 (B) YOLOv5n 640 28.4 46.0 45 6.3 0.6 1.9 4.5 YOLOv5s 640 37.2 56.0 98 6.4 0.9 7.2 16.5 YOLOv5m 640 45.2 63.9 224 8.2 1.7 21.2 49.0 YOLOv5l 640 48.8 67.2 430 10.1 2.7 46.5 109.1 YOLOv5x 640 50.7 68.9 766 12.1 4.8 86.7 205.7 YOLOv5n6 1280 34.0 50.7 153 8.1 2.1 3.2 4.6 YOLOv5s6 1280 44.5 63.0 385 8.2 3.6 16.8 12.6 YOLOv5m6 1280 51.0 69.0 887 11.1 6.8 35.7 50.0 YOLOv5l6 1280 53.6 71.6 1784 15.8 10.5 76.8 111.4 YOLOv5x6+ TTA 12801536 54.755.4 72.472.3 3136- 26.2- 19.4- 140.7- 209.8- Table Notes (click to expand) All checkpoints are trained to 300 epochs with default settings and hyperparameters. mAPval values are for single-model single-scale on COCO val2017 dataset.Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65 Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.Reproduce by python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 TTA Test Time Augmentation includes reflection and scale augmentations.Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment Contribute

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!

Contact

For YOLOv5 bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please visit https://ultralytics.com/contact.

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yolov5: yolov5官方源码

yolov5: yolov5官方源码

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