cgiar-wheat-growth-stage-challenge's Introduction

CGIAR-Wheat-Growth-Stage-Challenge

The goal of this competition is to predict the wheat growth stage using images. There are 7 growth stages (from 1 to 7). There are 2 types of labels : Expert labels (reliable) and Normal labels (less reliable). The Test set was annotated by experts. So, to train my model, I only used Expert labeled data.

Methodology

Model/Architecture

I used EfficientNetB3 as a backbone.
I added a fully connected layer on top of it (512, 256, 1) with a dropout of 0.3.
I treated the problem as a regression problem. The chosen loss function is MSE.
The chosen optimizer is ADAM with default parameters.
Image size = (512, 120)
I used cosine annealing as a learning rate scheduler.

Data augmentation

RandomBrightnessContrast
MotionBlur/MedianBlur/GaussianBlur
Horizontal/Vertical Flip
ShiftScaleRotate

Training/Inference

I splitted the dataset into 5-Folds stratified with respect to 'Growth Stage'. For each split, a model was trained. For inference, we make a prediction using each one of the 5 models and then, we average them.

Packages

Pytorch Lightning 0.9.1rc3

Setup

Download Images.zip and Train.csv from the link above and extract Images.zip in a folder named 'Images'.
Run 'train_folds.py' to train the models.
Run 'submission_folds.py BEST_FOLD1_PATH BEST_FOLD2_PATH BEST_FOLD3_PATH BEST_FOLD4_PATH BEST_FOLD5_PATH' to create the submission file.
Submit.

Result

Private Leaderboard Score : 0.44 (RMSE)

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hichemak / cgiar-wheat-growth-stage-challenge Goto Github PK