A simple multi-class classifier for 15 types of fruit.
Resnet18 and Resnet50 pretrained on Imagenet then finetuned using fruit images. In this project, I only replace the final linear layer of each network which might explain the slightly lower performance of Resnet50 compared to Resnet18.
Accuracy of the network on the test images: 96 %
Accuracy of Apple : 83 %
Accuracy of Banana : 100 %
Accuracy of Carambola : 97 %
Accuracy of Guava : 97 %
Accuracy of Kiwi : 88 %
Accuracy of Mango : 98 %
Accuracy of Muskmelon : 99 %
Accuracy of Orange : 100 %
Accuracy of Peach : 100 %
Accuracy of Pear : 98 %
Accuracy of Persimmon : 96 %
Accuracy of Pitaya : 100 %
Accuracy of Plum : 100 %
Accuracy of Pomegranate : 91 %
Accuracy of Tomatoes : 99 %
Accuracy of the network on the test images: 95 %
Accuracy of Apple : 78 %
Accuracy of Banana : 100 %
Accuracy of Carambola : 99 %
Accuracy of Guava : 98 %
Accuracy of Kiwi : 86 %
Accuracy of Mango : 97 %
Accuracy of Muskmelon : 98 %
Accuracy of Orange : 100 %
Accuracy of Peach : 97 %
Accuracy of Pear : 99 %
Accuracy of Persimmon : 98 %
Accuracy of Pitaya : 100 %
Accuracy of Plum : 100 %
Accuracy of Pomegranate : 90 %
Accuracy of Tomatoes : 97 %
Images from Kaggle Fruit Recognition Dataset(https://www.kaggle.com/chrisfilo/fruit-recognition)