Food image recognition using CNN

The advancement and innovation in technology such as android telephones and iPhone their computational force also get expanded. But the individuals are increasingly cognizant about their food and diet. We can utilize this innovation to assist individuals with arranging various sorts of food and their medical advantages. A new methodology is introduced to group food images utilizing convolutional neural systems. Not at all like the traditional artificial neural network, have convolutional neural systems had the ability of assessing the score work legitimately from picture pixels. There are numerous such layers, and the yields are concatenated at parts to frame the last tensor of yields. MAX pooling technique is used to separate essential features from the images and use it to train the model. In the proposed system we get an accuracy of 86.85% for the FOOD-101 dataset.