Object Detection
ultralytics
ONNX
TensorRT
Vietnamese
yolo
yolov8
torchscript
int8
fp16
vision
traffic-sign
vietnam
Instructions to use liamxdev/vtsr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use liamxdev/vtsr with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("liamxdev/vtsr", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - TensorRT
How to use liamxdev/vtsr with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download .gitattributes from liamxdev/vtsr: direct link, hf CLI and curl.
- Browser
- Download file 178 Bytes
-
https://huggingface.co/liamxdev/vtsr/resolve/main/.gitattributes
- Command line
-
hf download hf://liamxdev/vtsr/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/liamxdev/vtsr/resolve/main/.gitattributes
178 Bytes
| *.pt filter=lfs diff=lfs merge=lfs -text | |
| *.onnx filter=lfs diff=lfs merge=lfs -text | |
| *.engine filter=lfs diff=lfs merge=lfs -text | |
| *.torchscript filter=lfs diff=lfs merge=lfs -text |