Instructions to use SummerChiam/pond_image_classification_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerChiam/pond_image_classification_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SummerChiam/pond_image_classification_2") 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_2") model = AutoModelForImageClassification.from_pretrained("SummerChiam/pond_image_classification_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SummerChiam/pond_image_classification_2: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/SummerChiam/pond_image_classification_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SummerChiam/pond_image_classification_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SummerChiam/pond_image_classification_2/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 76d89d655add5b9a0c51b207b2ca3478a0411b171592ec9d0a6a7579d161b9d6
- Size of remote file:
- 343 MB
- SHA256:
- abf387d904d5479023515603573d5dec27cda47bde2f00aacc22fbc25dc1c6d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.