Instructions to use jameslahm/yolov10x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use jameslahm/yolov10x with YOLOv10:
from ultralytics import YOLOv10 model = YOLOv10.from_pretrained("jameslahm/yolov10x") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from jameslahm/yolov10x: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
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https://huggingface.co/jameslahm/yolov10x/resolve/main/README.md
- Command line
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hf download hf://jameslahm/yolov10x/README.md
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curl -L -o README.md https://huggingface.co/jameslahm/yolov10x/resolve/main/README.md
1.47 kB
metadata
license: agpl-3.0
tags:
- object-detection
- computer-vision
- yolov10
- pytorch_model_hub_mixin
datasets:
- detection-datasets/coco
library_name: yolov10
inference: false
Model Description
YOLOv10: Real-Time End-to-End Object Detection
Installation
pip install git+https://github.com/THU-MIG/yolov10.git
Training and validation
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10x')
# Training
model.train(...)
# after training, one can push to the hub
model.push_to_hub("your-hf-username/yolov10-finetuned")
# Validation
model.val(...)
Inference
Here's an end-to-end example showcasing inference on a cats image:
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10x')
source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
model.predict(source=source, save=True)
which shows:
BibTeX Entry and Citation Info
@article{wang2024yolov10,
title={YOLOv10: Real-Time End-to-End Object Detection},
author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
journal={arXiv preprint arXiv:2405.14458},
year={2024}
}
