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
File size: 1,191 Bytes
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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
│
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Image Processing
│
â–¼
OCR
(EasyOCR)
│
â–¼
Nutrition / Ingredient
Parsing
│
â–¼
CNN Processing
│
â–¼
Structured Analysis
│
â–¼
NutriVision App |