hariprasath-v / Zindi_Cgiar_crop_damage_classification_challenge

Crop damage classification

Zindi_Cgiar_crop_damage_classification_challenge

Competition hosted on Zindi

About

Create a machine-learning algorithm to classify crops into categories: Good growth (G), Drought (DR), Nutrient Deficient (ND), Weed (WD), and Other (including pest, disease or wind damage). The data for this challenge is a collection of smartphone images of crops.

The Final Competition score is 0.696384917

Final Leaderboard Rank is 152.

The Evaluation Metric is Log Loss.

File information

• EDA

  Basic image information analysis

  Images RGB color analysis

  Image similarity analysis

  Packages Used,

   * seaborn 
   * Pandas
   * Numpy
   * Matplotlib
   * imagehash
   * distance
   * Image
   * cv2
  

• Model

  Trained ViT Base 16 Patch 224 model on five-fold training data with various augmentations. Ten epochs were used to train the five-fold dataset, and early stopping was implemented to control overfitting by monitoring the validation log loss. The test data was predicted using the five-fold model, and test-time augmentation was applied to ensure confident predictions. The model's performance was tracked using WANDB.

  Model Log