Instructions to use avinashhm/welding-defect-yolov8-full-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use avinashhm/welding-defect-yolov8-full-training 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("avinashhm/welding-defect-yolov8-full-training", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download train_batch9182.jpg from avinashhm/welding-defect-yolov8-full-training: direct link, hf CLI and curl.
- Browser
- Download file 457 kB
-
https://huggingface.co/avinashhm/welding-defect-yolov8-full-training/resolve/main/train_batch9182.jpg
- Command line
-
hf download hf://avinashhm/welding-defect-yolov8-full-training/train_batch9182.jpg
-
curl -L -o train_batch9182.jpg https://huggingface.co/avinashhm/welding-defect-yolov8-full-training/resolve/main/train_batch9182.jpg
457 kB

- Xet hash:
- 68800488b5b4d83d15eebc97a4935e2563c75ae179933803119f8daacf4e59bc
- Size of remote file:
- 457 kB
- SHA256:
- 73dbcd7313459c6fd121f6d71d029f45143cd6217a05ebee16faca7971a9a961
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.