Gives the solution of Food101 Kaggle problem

CNN Image Classification Model which identifies the food in your image

An implementation of one personal project to gain experience with Generative Adversarial Network models and in particular on Wasserstrein GAN with gradient penalty. The final application's purpose is to generate synthetic images given a food category.

A neural network that can identify 101 different foods from an image. The "Final Model" section contains model weights for EfficientNetB2 + a classifier block that gives a top-1 accuracy of 73.2% and a top-5 accuracy of 91.2%. The models are all trained on the Food101 dataset.

A Food image classification of 101 classes with EfficientNetB0 and streamlit

An example of Chainer software that use Food-101 dataset

Food 101 Classification Model

a web App and Mobile app to classify food dishes and then to display recipe of that food

Deep Food Image Recognition Project

From preprocessing to building a CNN for the Food 101 Dataset

An end-to-end CNN Image Classification Model which identifies the food in your image

(Pattern Recognition Letters 2023) PyTorch implementation of "Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer"