lannguyen0910 / cassava_leaf_disease

Kaggle Cassava Leaf Disease Classification

🌿 Cassvana Leaf Disease Classification

Kaggle Competition: https://www.kaggle.com/c/cassava-leaf-disease-classification

Dataset

• Train set: ~26,000 images (21367 images of the 2020 contest was merged with 500 images from the 2019 contest).
• Test set: ~15,000 images.
• Public test: 31% of the test set.
• Private test: 69% of the test set.

The dataset is imbalanced with 5 labels:

1. Cassava Bacterial Blight (CBB)
2. Cassava Brown Streak Disease (CBSD)
3. Cassava Green Mottle (CGM)
4. Cassava Mosaic Disease (CMD)
5. Healthy

Original data images

After albumentations

Requirements

Python >= 3.8. Run this command to install all the dependencies:

pip install -r requirements.txt

Directories Structures

  this repo
  └───  train_images                        
  │     └───  ***.png                    # Dataset folder   
  └───  test_images                        
  │     └───  ***.png              
  |
  └───  configs                 # Config folder                                          
  │     └─── train.yaml
  │     └─── test.yaml
  │     └─── config.py
  |              
  └───  csv                   # labels folder               
  │     └─── folds
  │         └─── fold_train.csv
  │         └─── fold_val.csv
  │                     
  └───  loggers                    # experiments folder               
  │     └─── runs
  │         └─── loss_fold
  |         └─── acc_fold        
  └───  weights                    # experiments folder               
  │     └─── model_name1.pth 
  |     └───  ...   
  |     
  |            
  train.py
  test.py

Edit YAML

Full explanation on each YAML file

Training Steps

1. Download and unzip dataset from https://www.kaggle.com/c/cassava-leaf-disease-classification/data
2. Run this command to split train.csv using KFold. A folder name 'csv' will be created with different k-folds

python utils/tools/split_kfold.py --csv=train.csv --seed=42 --out=csv --n_splits=5 --shuffle=True 

3. Run this command on terminal or colaboratory (change if needed)

tensorboard --logdir='./loggers/runs'

1. Run this command and fine-tune on parameters for fully train observation (Require change)

python train.py --config=config_name --resume=weight_path --print_per_iters=100 --gradcam_visualization

5. The model weights will be saved automatically in the 'weights' folder

Inference

Run this command to generate predictions and submission file (Require fine-tune inside)

python test.py --config=test

Result

I have trained on Efficientnet-b6, EfficientNet-b1 and ViT. Here are the results:

1. The result from Efficientnet-b6 is not quite good, accuracy just between 0.7-0.8 before Early Stopping.
2. The result from Efficientnet-b1 and ViT are good enough: about 0.87x each.
3. Some visualization for Mosaic Disease with GradCam

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To-do list:

• Multi-GPU support (nn.DataParallel)
• GradCAM vizualization
• Gradient Accumulation
• Mixed precision
• Stratified KFold splitting
• Inference with Ensemble Model technique and TTA
• Metrics: Accuracy, Balanced Accuracy, F1-Score
• Losses: Focal Loss, SmoothCrossEntropy Loss
• Optimizer: AdamW, SGD. Adas, SAM (not debug yet)
• Scheduler: ReduceLROnPlateau, CosineAnnealingWarmRestarts
• Usable Models: Vit, EfficientNet, Resnext, Densenet
• Early Stopping on training

Reference:

Make sure to give them a star

• Template from: https://github.com/kaylode/custom-template
• timm models from https://github.com/rwightman/pytorch-image-models