Tensorflow implementation of "Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network"

A system that takes food images as an input, recognizes the food automatically and gives the nutritional-facts as an output.

Building a model for predicting food images using the famous FOOD-101 dataset.

training food-101 (achieved SOTA top-1 validation acc ~=90%) using 1-cycle-policy:

Vision Transformer Based Food-101 Classification

This repo contains implementations of the challenges from fellowship.ai. For more, visit here.

A clean and modular pipeline for preprocessing the Food-101 dataset using both folder-based and CSV-based workflows.

classifying the food101 dataset using cnn

Food classification on Food-101 dataset with optimization of training with TF Profiler, transfer learning, fine-tuning, intelligibility

Web application using Streamlit and Keras to predict food class.

Training a Deep neural network with torch- Application on food recognition

A deep learning project for food image classification using the Food-101 dataset, leveraging DenseNet201 architecture. Includes nutritional facts estimation with scope for improvement in accuracy and scalability.

Using Keras models and datasets to build custom prediction models

Fine-tuning a MobileNet model on the Food-101 dataset. Involves experimenting with different techniques for optimal performance, and features a Flask web application for real-time inference.

Example application for training Microsofts's pretrained BEiT image transformer model on a new image classification task

🥗 Discover healthy food options with this machine-learning recommender that analyzes nutrition data and delivers tailored suggestions for your goals.