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Uploaded image (dating)
Inspiration The inspiration for "Siblings or Dating" came from the viral social media trend where users post photos of pairs of people and ask others to guess their relationship. We wanted to create an AI-powered solution that could automate this guessing game while exploring the intersection of computer vision and human relationships. The subreddit r/siblingsordating, where users share ambiguous photos, served as a direct muse for this project.
What it does "Siblings or Dating" is a web application that classifies uploaded images of two people into siblings or dating using a trained machine learning model. Users upload a photo, and the system returns a prediction with a confidence score, sparking curiosity and engagement.
How we built it Data Collection:
Scraped labeled images from Reddit’s r/siblingsordating using PRAW.
Filtered images based on author comments to assign labels ("siblings" or "dating").
Model Training:
Built a binary classifier using TensorFlow and MobileNetV2 for transfer learning. Fine-tuned the model with data augmentation and class balancing.
Challenges we ran into Data Scraping: Reddit API rate limits and inconsistent labeling in user comments.
Ambiguous Labels: Many images had unclear or conflicting labels, requiring manual filtering.
Model Overfitting: Balancing accuracy with limited training data (300 images).
Accomplishments that we're proud of Achieved ~75% validation accuracy despite limited training data.
Built an end-to-end pipeline from data scraping to deployment.
Created a user-friendly interface that works seamlessly across devices.
Successfully leveraged transfer learning to adapt a pre-trained model to a niche use case.
What we learned Data is king: Clean, labeled data is critical for model performance.
Balancing act: Managing class weights and data augmentation to prevent bias.
What's next for Siblings or Dating Expand the Dataset: Collect more diverse images to improve generalization.
Mobile App: Develop a cross-platform app for on-the-go predictions.
Social Features: Allow users to vote on predictions and share results.
Model Explainability: Highlight facial features influencing predictions (e.g., posture, proximity).
Community Contributions: Let users submit labeled images to train future models.
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