Food Image Classification

Objective:

The main objective of the project is to build a Multi-Class Image Classifier to identify the food category, given an image of food.

Dataset:

The Dataset "Food-11 image dataset" is obtained from Kaggle (click here). This dataset contains 16643 food images grouped in 11 major food categories. There are 3 splits in this dataset:

• Training
• Validation
• Evaluation

Architecture Used:

The following Neural Network Architechture are used to build models using Transfer Learning in Python:

• Inception V3
• VGG 16

Evaluation:

The Metric used for evaluation is Accuracy.

Outcome:

The models obtained the following accuracy after fine tuning them

Model	Accuracy
VGG-16	86.13%
Inception V3	91.88%

Inception V3 was selected as the finalized model.