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This is a dataset containing 2500 food and 2500 non-food images, for the task of food/non-food classification in our paper “Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model”. The whole dataset is divided in three parts: training, validation and evaluation. The naming convention is as follows:
ClassID: 0 or 1; 0 means non-food and 1 means food.
ImageID: ID of the image within the class.
This is a dataset containing 16643 food images grouped in 11 major food categories. The 11 categories are Bread, Dairy product, Dessert, Egg, Fried food, Meat, Noodles/Pasta, Rice, Seafood, Soup, and Vegetable/Fruit. Similar as Food-5K dataset, the whole dataset is divided in three parts: training, validation and evaluation. The same naming convention is used, where ID 0-10 refers to the 11 food categories respectively.
Demonstration Android App
We have also developed an Android app, named NutriTake, to demonstrate the food classification and recognition. You can simply take a picture with your camera or from photo gallery, and our app will recognize the food in it. If the recognized food is wrong, you can correct the answer and our system will remember your correct label.
You can download the two datasets as well as the demonstration app from the following FTP (please use dedicated FTP clients, such as FileZilla or FireFTP):
FTP address: tremplin.epfl.ch
Username: [email protected]
FTP port: 21
After you connect, choose the FoodImage folder from the remote site, and download the relevant material. The total size of the Food-5K dataset is ~446.9 MB, while the total size of the Food-11 dataset is ~1.16 GB.