AI-Driven Clothing Similarity: Enhancing Fashion Ecommerce

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  Interface_Dashboard

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  Flow_Chart_Model

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  Interface_Dashboard_2

Inspiration

Our inspiration stems from the need to streamline fashion e-commerce by efficiently identifying duplicate and similar clothing items in large datasets.

What it does

Our project utilizes deep learning techniques to power a dashboard that identifies similarities within a dataset of 140,000 fashion images.

How we built it

The dashboard was crafted using Streamlit for its intuitive interface. We seamlessly integrated our proprietary deep learning model, harnessing the power of convolutional neural networks (CNNs) to analyze fashion images. While CNNs provided robust feature extraction capabilities, we encountered challenges in fine-tuning parameters to optimize accuracy and efficiency.

Challenges we ran into

The main challenge was managing the computational load of comparing tens of thousands of images.

Accomplishments that we're proud of

We're proud to have developed a solution that efficiently identifies clothing similarities, meeting the crucial criteria of the InditexTech Hackathon Challenge 2024.

What we learned

Through this project, we deepened our understanding of deep learning techniques, particularly in the context of fashion e-commerce.

What's next for AI-Driven Clothing Similarity: Enhancing Fashion Ecommerce

In the future, we aim to further optimize our model for even greater precision and scalability, advancing the capabilities of fashion e-commerce platforms.

Built With

• clothes
• cnn
• embeddings
• hpc
• python
• streamlit