Leaf Disease Classification and Concrete Crack Classification

About project

This are image classification problems. I will implement VGG-16 and LeNet - 2 simple convolutional neural networks to solve 2 prolems:

1. Classify cracks in images. (binary classification)
2. Classify 1 of 5 types of leaf's disease (multiclass classification)

This project using 2 frameworks: pytorch and tensorflow

1. With Leaf Disease datasets:

• Input: a 32x32x3 image
• Output:: this leaf belongs to 1 of 5 classes: CBB, CBSD, CGM, CMD, or healthy.

2. With Crack datasets:

• Input: a 227x227x3 image
• Output: whether there is a crack in image or not.

Model

1. Lenet

Lenet was introduced in 1998 by Yann LeCun LeNet-5 CNN architecture is made up of 7 layers. The layer composition consists of 3 convolutional layers, 2 subsampling layers and 2 fully connected layers:

Reference

1. https://d2l.ai/chapter_convolutional-neural-networks/lenet.html
2. https://towardsdatascience.com/understanding-and-implementing-lenet-5-cnn-architecture-deep-learning-a2d531ebc342
3. https://www.kaggle.com/code/blurredmachine/lenet-architecture-a-complete-guide
4. https://builtin.com/machine-learning/fully-connected-layer
5. https://www.analyticsvidhya.com/blog/2020/08/image-augmentation-on-the-fly-using-keras-imagedatagenerator/#:~:text=However%2C%20the%20main%20benefit%20of,still%20in%20the%20training%20stage.
6. https://tiensu.github.io/blog/33_dataaugmentation_modelcheckpoint_cnn/ (ModelCheckpoint)
7. https://majianglin2003.medium.com/train-neural-network-with-pytorch-5231c616d420