Competition hosted on Zindi
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.
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* seaborn * Pandas * Numpy * Matplotlib * imagehash * distance * Image * cv2
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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.