Instructions to use fxmarty/resnet-tiny-mnist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fxmarty/resnet-tiny-mnist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fxmarty/resnet-tiny-mnist") 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("fxmarty/resnet-tiny-mnist") model = AutoModelForImageClassification.from_pretrained("fxmarty/resnet-tiny-mnist", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fxmarty/resnet-tiny-mnist: direct link, hf CLI and curl.
- Browser
- Download file 763 kB
-
https://huggingface.co/fxmarty/resnet-tiny-mnist/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fxmarty/resnet-tiny-mnist/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fxmarty/resnet-tiny-mnist/resolve/main/pytorch_model.bin
763 kB
- Xet hash:
- 63358ecf922caebc078f3beeba344dfc5891e6fde8496af5d4a8b77d922e9115
- Size of remote file:
- 763 kB
- SHA256:
- 72b3ed2e1f131afbe98687a782109fa539b77a1b60713d8be2cb09dab092db7f
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