Instructions to use fxmarty/resnet-tiny-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fxmarty/resnet-tiny-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fxmarty/resnet-tiny-beans") 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-beans") model = AutoModelForImageClassification.from_pretrained("fxmarty/resnet-tiny-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR create with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. Feel free to ignore this PR.3f07997
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added onnx model
Felix Marty9e5efa5
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fix image size
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image size
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channels
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resize and crop
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add labels
Felix Martyd7744ce -
added model
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initial commit
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