You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
Ayush Chaurasia c1b38428bc
Update save_dir rank check (#114)
2 years ago
.github Model interface enhancement (#106) 2 years ago
docs Rename `img_size` to `imgsz` (#86) 2 years ago
tests Make YOLO a module (#111) 2 years ago
ultralytics Update save_dir rank check (#114) 2 years ago
.gitignore Integration of v8 segmentation (#107) 2 years ago
.pre-commit-config.yaml DDP and new dataloader Fix (#95) 2 years ago
CITATION.cff Fix CITATION.cff typos (#64) 2 years ago
CONTRIBUTING.md docs setup (#61) 2 years ago
LICENSE Initial commit 2 years ago
MANIFEST.in Trainer + Dataloaders (#27) 2 years ago
README.md Make YOLO a module (#111) 2 years ago
mkdocs.yml Update docs (#71) 2 years ago
requirements.txt Allocated updated pycocotools metrics fix (#101) 2 years ago
setup.cfg Flake8 updates (#66) 2 years ago
setup.py docs setup (#61) 2 years ago

README.md

Ultralytics CI

Install

pip install ultralytics

Development

git clone https://github.com/ultralytics/ultralytics
cd ultralytics
pip install -e .

Usage

1. CLI

To simply use the latest Ultralytics YOLO models

yolo task=detect    mode=train     model=yolov8n.yaml ...
          classify       predict         yolov8n-cls.yaml
          segment        val             yolov8n-seg.yaml

2. Python SDK

To use pythonic interface of Ultralytics YOLO model

from ultralytics import YOLO

model = YOLO.new('yolov8n.yaml')  # create a new model from scratch
model = YOLO.load('yolov8n.pt')  # load a pretrained model (recommended for best training results)

results = model.train(data='coco128.yaml', epochs=100, imgsz=640, ...)
results = model.val()
results = model.predict(source='bus.jpg')
success = model.export(format='onnx')

If you're looking to modify YOLO for R&D or to build on top of it, refer to Using Trainer Guide on our docs.