Instructions to use Amrrs/indian-foods with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amrrs/indian-foods with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Amrrs/indian-foods") 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("Amrrs/indian-foods") model = AutoModelForImageClassification.from_pretrained("Amrrs/indian-foods") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: indian-foods | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9285714030265808 | |
| # indian-foods | |
| 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 | |
| #### idli | |
|  | |
| #### kachori | |
|  | |
| #### pani puri | |
|  | |
| #### samosa | |
|  | |
| #### vada pav | |
|  |