Instructions to use sebchw/MNIST_Existing_Baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sebchw/MNIST_Existing_Baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sebchw/MNIST_Existing_Baseline") 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("sebchw/MNIST_Existing_Baseline") model = AutoModelForImageClassification.from_pretrained("sebchw/MNIST_Existing_Baseline") - Notebooks
- Google Colab
- Kaggle
add model
Browse files- pytorch_model.bin +3 -0
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:72b3ed2e1f131afbe98687a782109fa539b77a1b60713d8be2cb09dab092db7f
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size 763481
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