hariprasath-v / AV_wns-triange-hackquest

Develope a robust and high-performance model utilizing computer vision techniques to classify images as either fraudulent or non-fraudulent within the context of insurance claims

AV_wns-triange-hackquest

Competition hosted on Analyticsvidhya

About

Develope a robust and high-performance model utilizing computer vision techniques to classify images as either fraudulent or non-fraudulent within the context of insurance claims. By precisely identifying fraudulent images, insurance companies can evaluate the authenticity of a claim and make well-informed decisions regarding payout.

The Final Competition score is 0.7834901167

Final Leaderboard Rank is 68/253.

The Evaluation Metric is macro f1-score.

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 efficientvit_b3_r224 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