corn-plant-disease-classification

corn plant disease classification with pytorch

Requierement

• Pytorch
• torchvision
• torchsummary : to print the model's summary in keras style
• ray-tune : for hyperparameters tuning

Dataset

base dataset consists of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes. We only need corn pictures,the data_proceesing.py script creates a new dataset containing only corn images, keeping the same structure as the initial dataset

data_processing.py [-h] [--data_dir DATA_DIR]

dataset processing

options:
  -h, --help           show this help message and exit
  --data_dir DATA_DIR  Path to the data directory
 

data_dir is the path to the dataset folder which contains Train and valid folders

if the path to the dataset is not specified, data_dir=dataset_dir="./data/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)"

a corn_data folder is created in the current project folder.

Training

the architecture of the model(resnet) to train is defined in the model.py file and configuration parameters in config.json

Train the model

   python train.py

Train and save in current project folder.

    python train.py --save

batch size and epochs are configurable with config.json Instead of using a fixed learning rate, we will use a learning rate scheduler, max_lr in config.json is max learning rate. the other parameters are Weight decay ,Gradient clipping,

this strategy is better explained on my kaggle notebook about this project.

find the notebbok here