Yolov5

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YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open - source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! To request an Enterprise License please complete the form at Ultralytics Licensing.

✨ Features

YOLO11 🚀 NEW

We are excited to unveil the launch of Ultralytics YOLO11 🚀, the latest advancement in our state - of - the - art (SOTA) vision models! Available now at GitHub, YOLO11 builds on our legacy of speed, precision, and ease of use. Whether you're tackling object detection, image segmentation, or image classification, YOLO11 delivers the performance and versatility needed to excel in diverse applications. Get started today and unlock the full potential of YOLO11! Visit the Ultralytics Docs for comprehensive guides and resources:

pip install ultralytics

🚀 Quick Start

Installation

Clone repo and install requirements.txt in a Python>=3.8.0 environment, including PyTorch>=1.8.

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

Inference

PyTorch Hub Inference

YOLOv5 PyTorch Hub inference. Models download automatically from the latest YOLOv5 release.

import torch

# Model
model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, 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 --weights yolov5s.pt --source 0                               # webcam
                                               img.jpg                         # image
                                               vid.mp4                         # video
                                               screen                          # screenshot
                                               path/                           # directory
                                               list.txt                        # list of images
                                               list.streams                    # list of streams
                                               'path/*.jpg'                    # glob
                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Training

The commands below reproduce YOLOv5 COCO results. Models and datasets download automatically from the latest YOLOv5 release. Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU (Multi - GPU times faster). Use the largest --batch - size possible, or pass --batch - size - 1 for YOLOv5 AutoBatch. Batch sizes shown for V100 - 16GB.

python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
                                                                 yolov5s                    64
                                                                 yolov5m                    40
                                                                 yolov5l                    24
                                                                 yolov5x                    16

📚 Documentation

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

Tutorials

• Train Custom Data 🚀 RECOMMENDED
• Tips for Best Training Results ☘️
• Multi - GPU Training
• PyTorch Hub 🌟 NEW
• TFLite, ONNX, CoreML, TensorRT Export 🚀
• NVIDIA Jetson platform Deployment 🌟 NEW
• Test - Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers
• Architecture Summary 🌟 NEW
• Ultralytics HUB to train and deploy YOLO 🚀 RECOMMENDED
• ClearML Logging
• YOLOv5 with Neural Magic's Deepsparse
• Comet Logging 🌟 NEW

🤝 Integrations

Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with W&B, Comet, Roboflow

📄 License

This project is licensed under the AGPL - 3.0 license.