Instructions to use W4ashabii/waste_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use W4ashabii/waste_classifier 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("W4ashabii/waste_classifier", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download results.png from W4ashabii/waste_classifier: direct link, hf CLI and curl.
- Browser
- Download file 156 kB
-
https://huggingface.co/W4ashabii/waste_classifier/resolve/main/results.png
- Command line
-
hf download hf://W4ashabii/waste_classifier/results.png
-
curl -L -o results.png https://huggingface.co/W4ashabii/waste_classifier/resolve/main/results.png
156 kB

- Xet hash:
- 998f3eed358d46ab981ccbba616c0bbedb6b19b1842f91b85f6646c023c20620
- Size of remote file:
- 156 kB
- SHA256:
- 7a51b79e5a8bd835bfe728daf51c011ab8da02d484e8331a75656eae5d7bfd9c
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