Cassava-Dataset-Image-Classification

The dataset consists of leaf images of the cassava plant, with 9,436 annotated images and 12,595 unlabeled ones.The goal is to learn a model to classify a given image into 4 disease categories or a 5th category indicating a healthy leaf. 🍃 🍂 🍁

Methodology

Preprocessing

• A standard Augmentation.
• Cropped image size is 448.

Model and Hyper-Parameters

• The model is resnext50.
• The learning rate is 1e-4
• The batch size is 20.

Results

The model got 91.3% in public leaderboard.

Getting started

Just run this command,
python train.py --fold FOLD_NO --scheduler SCHEDULER --model MODEL where,

• MODEL is the model and currently can take the following values,
  • resnext_50 which is resnext50_32x4d
• SCHEDULER is the learning scheduler, and currently can take the following values,
  • cosine_1 which is CosineAnnealingLR
• FOLD_NO which is the fold number and take the values [0,1,2,3,4].

Note

before run the above command make sure to get the images from cassava_images and put in the folder data.