Instructions to use SummerChiam/pond_image_classification_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerChiam/pond_image_classification_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SummerChiam/pond_image_classification_4") 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("SummerChiam/pond_image_classification_4") model = AutoModelForImageClassification.from_pretrained("SummerChiam/pond_image_classification_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SummerChiam/pond_image_classification_4: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/SummerChiam/pond_image_classification_4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SummerChiam/pond_image_classification_4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SummerChiam/pond_image_classification_4/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 0e6f92d24c394e6366f2e10af28de00c505385b982380b0adf451b808350cddd
- Size of remote file:
- 343 MB
- SHA256:
- 48a489c453bb50458081550e53dfa00bcd7d7c9413881ccd68857a7d2ad1a535
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