Instructions to use hagerty7/recyclable-materials-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hagerty7/recyclable-materials-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hagerty7/recyclable-materials-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hagerty7/recyclable-materials-classification") model = AutoModelForImageClassification.from_pretrained("hagerty7/recyclable-materials-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hagerty7/recyclable-materials-classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/hagerty7/recyclable-materials-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hagerty7/recyclable-materials-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hagerty7/recyclable-materials-classification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 1bcf6ad0c3d481e9f96fa389d14f28a92abf7cc7a5a0435d2980d8c4a7d2768d
- Size of remote file:
- 343 MB
- SHA256:
- 59f8a36f0bb2d2b537b28fc6bfadbc93d7f9dd9fd9839083a26ec8a37f1a48ad
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