Instructions to use SummerChiam/rust_image_classification_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerChiam/rust_image_classification_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SummerChiam/rust_image_classification_6") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("SummerChiam/rust_image_classification_6") model = AutoModelForImageClassification.from_pretrained("SummerChiam/rust_image_classification_6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SummerChiam/rust_image_classification_6: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/SummerChiam/rust_image_classification_6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SummerChiam/rust_image_classification_6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SummerChiam/rust_image_classification_6/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 374d86983b6105bdac5d01fed033cfd4c75b3a2acf445d897e1c487298d2c77b
- Size of remote file:
- 343 MB
- SHA256:
- 37bdcf653263efb210a3c59af90fb7e249186b109cf3f118401571f675bd6ab8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.