Instructions to use Kaludi/Food-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaludi/Food-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kaludi/Food-Classification") 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("Kaludi/Food-Classification") model = AutoModelForImageClassification.from_pretrained("Kaludi/Food-Classification") - Notebooks
- Google Colab
- Kaggle
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# Food Classification
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This is a Food Image Classifier model that has been trained to recognize 7 different types of popular foods, including **apple pie**, **falafel**, **french toast**, **ice cream**, **ramen**, **sushi**, and **tiramisu**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.
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## Validation Metrics
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# Food Classification
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This is a Food Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 7 different types of popular foods, including **apple pie**, **falafel**, **french toast**, **ice cream**, **ramen**, **sushi**, and **tiramisu**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.
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## Validation Metrics
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