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
Update README.md
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README.md
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license: gpl-3.0
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license: gpl-3.0
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library_name: tensorflow
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tags:
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- tensorflow
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- keras
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- image-classification
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- computer-vision
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- food
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- nutrition
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- nutrivision
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# NutriVision CNN Model
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This repository contains the trained CNN model used by **NutriVision**, a mobile application designed to analyze food and nutrition labels from images.
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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.
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## About NutriVision
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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.
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The overall pipeline looks roughly like this:
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```text
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Food / Nutrition Label
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│
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▼
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Camera Image
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│
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Image Processing
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│
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▼
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OCR
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(EasyOCR)
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│
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▼
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Nutrition / Ingredient
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Parsing
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│
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▼
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CNN Processing
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│
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Structured Analysis
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│
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NutriVision App
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