ultralytics/yolov5

Clone repo and install https://github.com/ultralytics/yolov5/blob/master/requirements.txt in a Python>=3.8.0 environment, including PyTorch>=1.8.

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

YOLOv5 PyTorch Hub inference. https://github.com/ultralytics/yolov5/tree/master/models download automatically from the latest YOLOv5 https://github.com/ultralytics/yolov5/releases.

python
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.

detect.py runs inference on a variety of sources, downloading https://github.com/ultralytics/yolov5/tree/master/models automatically from the latest YOLOv5 https://github.com/ultralytics/yolov5/releases and saving results to runs/detect.

bash
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

The commands below reproduce YOLOv5 https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh results. https://github.com/ultralytics/yolov5/tree/master/models and https://github.com/ultralytics/yolov5/tree/master/data download automatically from the latest YOLOv5 https://github.com/ultralytics/yolov5/releases. 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 https://github.com/ultralytics/yolov5/pull/5092. Batch sizes shown for V100-16GB.

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

• 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
• Roboflow for Datasets, Labeling, and Active Learning
• ClearML Logging 🌟 NEW
• YOLOv5 with Neural Magic's Deepsparse 🌟 NEW
• Comet Logging 🌟 NEW