Instructions to use prithivMLmods/Augmented-Waste-Classifier-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Augmented-Waste-Classifier-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Augmented-Waste-Classifier-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Augmented-Waste-Classifier-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Augmented-Waste-Classifier-SigLIP2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1209/optimizer.pt from prithivMLmods/Augmented-Waste-Classifier-SigLIP2: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Augmented-Waste-Classifier-SigLIP2/resolve/main/checkpoint-1209/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Augmented-Waste-Classifier-SigLIP2/checkpoint-1209/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Augmented-Waste-Classifier-SigLIP2/resolve/main/checkpoint-1209/optimizer.pt
687 MB
- Xet hash:
- 2dde90eaf5db27844874fd32e9331dd28361060821158e3509af5742da533010
- Size of remote file:
- 687 MB
- SHA256:
- 37bb3664a98cc08b1c49cc495889c732902f196ddf12d1e950a00ac1f7d1c40e
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