Instructions to use ViTAMIn-O/PDLO_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViTAMIn-O/PDLO_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ViTAMIn-O/PDLO_classifier") 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("ViTAMIn-O/PDLO_classifier") model = AutoModelForImageClassification.from_pretrained("ViTAMIn-O/PDLO_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ViTAMIn-O/PDLO_classifier: direct link, hf CLI and curl.
- Browser
- Download file 781 MB
-
https://huggingface.co/ViTAMIn-O/PDLO_classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://ViTAMIn-O/PDLO_classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ViTAMIn-O/PDLO_classifier/resolve/main/model.safetensors
781 MB
- Xet hash:
- dcfa4472a8b4cb2e8a77122c880d4b002e3d43e58f4a256f535bed19e3a29542
- Size of remote file:
- 781 MB
- SHA256:
- add4f927138a0ebb8ed8fa8ac9ad893479f3a69eadafba1828b4dc3cd2591b95
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