Instructions to use SummerChiam/pond_image_classification_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerChiam/pond_image_classification_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SummerChiam/pond_image_classification_3") 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_3") model = AutoModelForImageClassification.from_pretrained("SummerChiam/pond_image_classification_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SummerChiam/pond_image_classification_3: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/SummerChiam/pond_image_classification_3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SummerChiam/pond_image_classification_3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SummerChiam/pond_image_classification_3/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 2563b5ee8286e80492b7a0e0641af00184c6bb03a22731934929e90eedbea2dc
- Size of remote file:
- 343 MB
- SHA256:
- bf52b0ea96e01daeb7a1dc515df802ceccf9d306361704ddf660e5e1b7f56f0c
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