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fruit_classifier

A simple multi-class classifier for 15 types of fruit.

Transfer Learning

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.

Results

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 %

Confusion Matrix

Confusion Matrix for Resnet18

Loss and Accuracy

Training vs Validation Accuracy for Resnet18 Training vs Validation Loss for Resnet18

Resnet50

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 %

Confusion Matrix

Confusion Matrix for Resnet50

Loss and Accuracy

Training vs Validation Accuracy for Resnet50 Training vs Validation Loss for Resnet50

Acknowledgements

Images from Kaggle Fruit Recognition Dataset(https://www.kaggle.com/chrisfilo/fruit-recognition)

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A simple multi-class classifier for 15 types of fruit.

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