Laughing 340376f7a6
Fix resume (#138)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2023-01-03 18:36:22 +05:30
2023-01-03 18:36:22 +05:30
2022-12-05 12:17:25 -08:00
2022-12-05 06:04:57 +05:30
2022-09-11 19:39:46 +03:00
2022-10-10 14:01:07 +02:00
2022-12-30 21:33:43 +01:00
2022-12-06 15:09:53 -08:00
2022-12-05 06:04:57 +05:30

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      args=...
          classify       predict        yolov8n-cls.yaml  args=...
          segment        val            yolov8n-seg.yaml  args=...
                         export         yolov8n.pt        format=onnx

2. Python SDK

To use pythonic interface of Ultralytics YOLO model

from ultralytics import YOLO

model = YOLO("yolov8n.yaml")  # create a new model from scratch
model = YOLO(
    "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.

Description
YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]
Readme AGPL-3.0 Cite this repository 14 MiB
Languages
Python 99.4%
Shell 0.3%
Dockerfile 0.3%