Cover image for Scientific Image Forgery Detection ā€” Kaggle Competition Part...
Scientific Image Forgery Detection ā€” Kaggle Competition Participated in the ongoing Kaggle competition on Copy-Move Forgery Detection in Scientific Images, aimed at identifying manipulated biomedical figures that can compromise research integrity. For this challenge, I developed a ResNet50 + U-Net hybrid segmentation model using PyTorch, designed to detect and segment forged regions at the pixel level. My approach combines Dice and Focal losses for balanced training, WeightedRandomSampling to oversample forged images, and Test-Time Augmentation (TTA) to improve prediction robustness. Achieved an initial score of 0.303 on the public leaderboard. Iā€™m continuing to experiment with architecture tuning, learning rate schedules, and other loss functions to further enhance performance and generalization.
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Cover image for Hi Contra community! Just wrapped a
Hi Contra community! Just wrapped a rewarding project for Nauvyashreeā€”delivered deep-dive EDA, custom visualizations, and rich survey sentiment analysis across four datasets. Each milestone included well-designed PDFs packed with clear charts and approachable explanationsā€”making insights accessible for all audiences. I went the extra mile with collaborative Google Meet sessions to walk the client through every analysis, helping connect the findings to big-picture goals. Feedback was fantastic, and milestone-based payments made the process smooth and transparent. This project really strengthened my ability to turn complex results into stories that empower client decision-making. If you want visually engaging, client-focused analyticsā€”or need easy-to-follow reports and hands-on walkthroughs for your next projectā€”letā€™s connect!
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