undefinedPython>=3.6.0undefined is required with all
requirements.txt installed including
undefinedPyTorch>=1.7undefined:

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

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.

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 video
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

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

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️
  RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Supervisely Ecosystem  🌟 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

• GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size
  32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
• EfficientDet data from google/automl at batch size 8.
• undefinedReproduce by
  python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

• AP^test denotes COCO test-dev2017 server results, all other AP results
  denote val2017 accuracy.
• AP values are for single-model single-scale unless otherwise noted. Reproduce mAPundefined
  by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
• Speed_GPU averaged over 5000 COCO val2017 images using a
  GCP n1-standard-16 V100 instance, and
  includes FP16 inference, postprocessing and NMS. Reproduce speedundefined
  by python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half
• All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
• Test Time Augmentation (TTA) includes reflection and scale
  augmentation. Reproduce TTA by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment