Fruit-Classification-Project
Exploration into CNN classification using the Keras API and fruit-360 dataset from Kaggle In this project using the Tensorflow Keras API I create a series of CNN models and evaluate the best one on the Fruit-360 dataset from Kaggle which contains 131 fruits and vegetables to classify. In addition I implement the 3 best models into pytorch framework.
Ultimately the best model was a transfer learning model based on pretrained weights from ResNet50
Link to model Files: https://drive.google.com/file/d/1NQjai2o9rIqRwpoV6iCJ8sOamJs0_K1e/view?usp=sharing
Custom CNN Model
This model has 4 convolutional layers and 2 fully connected layers. The fully connected layers implement dropout as a method of regularization.
Confusion Matrix as well as score on dataset:
Loss and Accuracy Per Epoch:
Test Image Batch:
Green means Passed, Red for Fail
Transfer Learning
Transfer Learning is done on ResNet50 pretrained on Imagenet. The features computed by ResNet is average pooled and passed into two fully connected layer where softmax layer outputs predictions.
Confusion Matrix as well as score on dataset:
Loss and Accuracy Per Epoch:
Test Image Batch:
Green means Passed, Red for Fail