Instructions to use Nonem100/Test-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nonem100/Test-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Nonem100/Test-Model") 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("Nonem100/Test-Model") model = AutoModelForImageClassification.from_pretrained("Nonem100/Test-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Nonem100/Test-Model: direct link, hf CLI and curl.
- Browser
- Download file 893 Bytes
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https://huggingface.co/Nonem100/Test-Model/resolve/main/README.md
- Command line
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hf download hf://Nonem100/Test-Model/README.md
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curl -L -o README.md https://huggingface.co/Nonem100/Test-Model/resolve/main/README.md
893 Bytes
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Test-Model | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9017857313156128 | |
| # Test-Model | |
| Autogenerated by HuggingPics🤗🖼️ | |
| Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). | |
| Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). | |
| ## Example Images | |
| #### cotton candy | |
|  | |
| #### hamburger | |
|  | |
| #### hot dog | |
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| #### nachos | |
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| #### popcorn | |
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