Clone the repository and install dependencies in a Python>=3.8.0 environment. Ensure you have PyTorch>=1.8 installed.

# Clone the YOLOv5 repository
git clone https://github.com/ultralytics/yolov5
Navigate to the cloned directory
cd yolov5
Install required packages
pip install -r requirements.txt

Use YOLOv5 via PyTorch Hub for inference. Models are automatically downloaded from the latest YOLOv5 release.

import torch
Load a YOLOv5 model (options: yolov5n, yolov5s, yolov5m, yolov5l, yolov5x)
model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # Default: yolov5s
Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list)
img = "https://ultralytics.com/images/zidane.jpg"  # Example image
Perform inference (handles batching, resizing, normalization automatically)
results = model(img)
Process the results (options: .print(), .show(), .save(), .crop(), .pandas())
results.print()  # Print results to console
results.show()  # Display results in a window
results.save()  # Save results to runs/detect/exp

The detect.py script runs inference on various sources. It automatically downloads models from the latest YOLOv5 release and saves the results to the runs/detect directory.

# Run inference using a webcam
python detect.py --weights yolov5s.pt --source 0
Run inference on a local image file
python detect.py --weights yolov5s.pt --source img.jpg
Run inference on a local video file
python detect.py --weights yolov5s.pt --source vid.mp4
Run inference on a screen capture
python detect.py --weights yolov5s.pt --source screen
Run inference on a directory of images
python detect.py --weights yolov5s.pt --source path/to/images/
Run inference on a text file listing image paths
python detect.py --weights yolov5s.pt --source list.txt
Run inference on a text file listing stream URLs
python detect.py --weights yolov5s.pt --source list.streams
Run inference using a glob pattern for images
python detect.py --weights yolov5s.pt --source 'path/to/*.jpg'
Run inference on a YouTube video URL
python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4'
Run inference on an RTSP, RTMP, or HTTP stream
python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4'

The commands below demonstrate how to reproduce YOLOv5 COCO dataset results. Both models and datasets are downloaded automatically from the latest YOLOv5 release. Training times for YOLOv5n/s/m/l/x are approximately 1/2/4/6/8 days on a single NVIDIA V100 GPU. Using Multi-GPU training can significantly reduce training time. Use the largest --batch-size your hardware allows, or use --batch-size -1 for YOLOv5 AutoBatch. The batch sizes shown below are for V100-16GB GPUs.

# Train YOLOv5n on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
Train YOLOv5s on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64
Train YOLOv5m on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40
Train YOLOv5l on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24
Train YOLOv5x on COCO for 300 epochs
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16

• Train Custom Data 🚀 RECOMMENDED: Learn how to train YOLOv5 on your own datasets.
• Tips for Best Training Results ☘️: Improve your model's performance with expert tips.
• Multi-GPU Training: Speed up training using multiple GPUs.
• PyTorch Hub Integration: Easily load models using PyTorch Hub.
• Model Export (TFLite, ONNX, CoreML, TensorRT) 🚀: Convert your models to various deployment formats like ONNX or TensorRT.
• NVIDIA Jetson Deployment: Deploy YOLOv5 on NVIDIA Jetson devices.
• Test-Time Augmentation (TTA): Enhance prediction accuracy with TTA.
• Model Ensembling: Combine multiple models for better performance.
• Model Pruning/Sparsity: Optimize models for size and speed.
• Hyperparameter Evolution: Automatically find the best training hyperparameters.
• Transfer Learning with Frozen Layers: Adapt pretrained models to new tasks efficiently using transfer learning.
• Architecture Summary: Understand the YOLOv5 model architecture.
• Ultralytics Platform Training 🚀 RECOMMENDED: Train and deploy YOLO models using Ultralytics Platform.
• ClearML Logging: Integrate with ClearML for experiment tracking.
• Neural Magic DeepSparse Integration: Accelerate inference with DeepSparse.
• Comet Logging: Log experiments using Comet ML.

• COCO AP val denotes the mean Average Precision (mAP) at Intersection over Union (IoU) thresholds from 0.5 to 0.95, measured on the 5,000-image COCO val2017 dataset across various inference sizes (256 to 1536 pixels).
• GPU Speed measures the average inference time per image on the COCO val2017 dataset using an AWS p3.2xlarge V100 instance with a batch size of 32.
• EfficientDet data is sourced from the google/automl repository at batch size 8.
• Reproduce these results using the command: python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

• The 640-pixel rows report the legacy anchor-based YOLOv5 checkpoints, while their links open the corresponding anchor-free YOLOv5u successor models on Ultralytics Platform. P6 links continue to download the legacy 1280-pixel checkpoints directly.
• All checkpoints were trained for 300 epochs using default settings. Nano (n) and Small (s) models use hyp.scratch-low.yaml hyperparameters, while Medium (m), Large (l), and Extra-Large (x) models use hyp.scratch-high.yaml.
• mAP^val values represent single-model, single-scale performance on the COCO val2017 dataset.
  Reproduce using: python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
• Speed metrics are averaged over COCO val images using an AWS p3.2xlarge V100 instance. Non-Maximum Suppression (NMS) time (~1 ms/image) is not included.
  Reproduce using: python val.py --data coco.yaml --img 640 --task speed --batch 1
• TTA (Test Time Augmentation) includes reflection and scale augmentations for improved accuracy.
  Reproduce using: python val.py --data coco.yaml --img 1536 --iou 0.7 --augment