Food-Vision

A Deep Leaning project using Computer Vision and Transfer-Leaning

• Downloading and preparing 10% of the Food101 data (10% of training data)
• Training a feature extraction transfer learning model on 10% of the Food101 training data
• Fine-tuning our feature extraction model
• Saving and loaded our trained model
• Evaluating the performance of our Food Vision model trained on 10% of the training data
• Finding our model's most wrong predictions
• Making predictions with our Food Vision model on custom images of food

Pre-Trained Model

EfficientNet’s layers are based on a compound scaling method that uniformly scales the depth, width, and resolution of the network, with the stem layer being the initial part of the network that performs initial convolutions to process the input image before deeper layers.

• https://miro.medium.com/v2/resize:fit:828/format:webp/1*TS6wCKBstPwWIfUpL_6-gw.png

Paper-Used

Food-101 https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf *