Instructions to use TweeeZT/Nutrivision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use TweeeZT/Nutrivision with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://TweeeZT/Nutrivision") - Notebooks
- Google Colab
- Kaggle
| license: gpl-3.0 | |
| library_name: tensorflow | |
| tags: | |
| - tensorflow | |
| - keras | |
| - image-classification | |
| - computer-vision | |
| - food | |
| - nutrition | |
| - nutrivision | |
| # NutriVision CNN Model | |
| This repository contains the trained CNN model used by **NutriVision**, a mobile application designed to analyze food and nutrition labels from images. | |
| The model is distributed separately from the main NutriVision source repository because the trained weights are a relatively large binary file and are better suited to dedicated model hosting. | |
| ## About NutriVision | |
| NutriVision is a full-stack mobile application that combines image processing, OCR, machine learning, and nutrition/ingredient parsing to turn information from food packaging into structured results that are easier to understand. | |
| The overall pipeline looks roughly like this: | |
| ```text | |
| Food / Nutrition Label | |
| │ | |
| â–¼ | |
| Camera Image | |
| │ | |
| â–¼ | |
| Image Processing | |
| │ | |
| â–¼ | |
| OCR | |
| (EasyOCR) | |
| │ | |
| â–¼ | |
| Nutrition / Ingredient | |
| Parsing | |
| │ | |
| â–¼ | |
| CNN Processing | |
| │ | |
| â–¼ | |
| Structured Analysis | |
| │ | |
| â–¼ | |
| NutriVision App |