Instructions to use TGrote11/Handwriting_Math_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TGrote11/Handwriting_Math_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TGrote11/Handwriting_Math_Classification") 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("TGrote11/Handwriting_Math_Classification") model = AutoModelForImageClassification.from_pretrained("TGrote11/Handwriting_Math_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from TGrote11/Handwriting_Math_Classification: direct link, hf CLI and curl.
- Browser
- Download file 211 Bytes
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https://huggingface.co/TGrote11/Handwriting_Math_Classification/resolve/main/README.md
- Command line
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hf download hf://TGrote11/Handwriting_Math_Classification/README.md
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curl -L -o README.md https://huggingface.co/TGrote11/Handwriting_Math_Classification/resolve/main/README.md
211 Bytes
| pipeline_tag: image-classification | |
| tags: | |
| - pytorch_model_hub_mixin | |
| - model_hub_mixin | |
| This model has been pushed to the Hub using ****: | |
| - Repo: [More Information Needed] | |
| - Docs: [More Information Needed] |