AI-Powered Smart Wardrobe and Outfit Recommendation System

Abstract

Ever stared at your closet and thought, “Why do I keep reaching for the same old shirt?” Yeah, you’re not alone. Managing a growing wardrobe turns into this messy daily scramble, and honestly, it gets annoying fast. That’s why we built an AI-powered Smart Wardrobe and Outfit Recommendation System—to help you keep track of your clothes and make picking out an outfit feel less like a chore. Here’s how it actually works: Just snap a picture of your clothes and upload them. Our system sorts everything into 15 categories using the ConvNeXt-Large deep learning model. We picked this one because, during early tests, it handled tricky lookalikes way better than other models—like telling apart casual shirts from polos, which most systems can’t seem to figure out. Instead of spitting out generic fashion advice, the system leans on a lightweight neural compatibility model. It looks at real, curated examples to learn what pieces fit together naturally. So when it suggests an outfit, it’s not just random—it’s actually something you’d want to wear. The technical setup is pretty simple. The React frontend lets you manage your digital closet, and the Flask backend does all the image crunching and AI calculations. The model isn’t perfect—it can get confused when your clothes are nearly identical—but overall, it nails classification and serves up outfit ideas people genuinely like. At the end of the day, this isn’t just about making life easier. We’re showing how AI can take the stress out of getting dressed and laying the groundwork for even smarter, more personal fashion tools in the future.

Introduction

The text discusses how deep learning and computer vision have improved fashion-related tasks such as clothing classification and outfit recommendation, but highlights that many existing systems still rely on traditional collaborative filtering or rule-based methods that ignore visual features of clothing. This limits their ability to handle real-world wardrobe images and generate personalized, visually coherent outfit suggestions.

With the availability of large datasets like DeepFashion and Polyvore, CNN-based models (such as ConvNeXt and EfficientNet) have become central to learning visual features directly from clothing images. However, challenges remain due to differences between clean catalog images and messy user-uploaded photos, including lighting variations, occlusions, and cluttered backgrounds. Multimodal models like CLIP also struggle with fine-grained garment compatibility in personal wardrobe scenarios.

To address these issues, the proposed system integrates clothing classification and outfit recommendation into a single pipeline for personal wardrobe management. It uses a balanced 15-class Kaggle dataset and trains a ConvNeXt-Large model with data augmentation and EfficientNet-style preprocessing to improve robustness. A lightweight neural compatibility module is also introduced to learn relationships between top and bottom clothing items and generate ranked outfit suggestions.

The methodology includes dataset preparation (7,500 images across 15 categories), preprocessing (resizing, normalization, augmentation), and a two-stage training strategy for ConvNeXt-Large (feature learning followed by fine-tuning). Performance is evaluated using Top-1, Top-3, and Top-5 accuracy.

Finally, the system combines a deep CNN-based classifier with a simple compatibility model to provide an end-to-end AI-powered wardrobe assistant that can classify clothing items and suggest suitable outfit combinations, aiming for practical use in real-world digital wardrobe applications.

Conclusion

In this work, we’ve developed a complete AI-powered wardrobe assistant that bridges the gap between deep learning classification and neural outfit recommendations. By leverag-ing the ConvNeXt-Large architecture for feature extraction, our system reached a peak validation accuracy of 87.29 Beyond just identifying clothes, our compatibility model successfully picked up on the nuances of top-and-bottom pairings by learning directly from curated examples. This allows the system to generate suggestions that actually make sense for a user’s closet. With a React frontend and a Flask backend working in tandem, the entire pipeline—from photo upload to the final outfit recommendation—is smooth and responsive. While our results are promising, we did notice that performance can dip when faced with tricky lighting or items that look nearly identical. These edge cases really highlight why future iterations will need even more diverse datasets and a deeper sense of contextual awareness. Ultimately, our framework shows that pairing modern convolutional models with lightweight recommendation engines is a highly scalable way to help people organize their wardrobes and discover new styles.

References

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Copyright

Copyright © 2026 Keshav Sonawane, Atharva Kotkar, Satyam Bartakke, Rugved Shinde, Dr. Vaishali Suryawanshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.