apoorvjain25/techolution_test

This project, called Techolution_test, is designed to find duplicate items in a large product catalog. If you have an online store with hundreds of thousands of products, you might accidentally have multiple listings for the exact same item. This tool helps identify those duplicates by looking at text data and analyzing the actual product images, so you can clean up your inventory and avoid confusing your customers. The project takes three different approaches to solving this problem. The first is a straightforward method that searches a massive spreadsheet of product data to find matching entries. The second and third approaches use computer vision, which means the code actually looks at the pictures of the products. One method compares the colors in the images, while the other uses a deep learning model to recognize the actual objects in the photos, providing a more accurate comparison. Someone managing an e-commerce platform or a large digital asset library would use this to automatically filter out redundant listings. For example, if two different sellers upload a photo of a very similar red chair with the exact same background, the system can flag them as potential duplicates. The creator notes that this deep learning approach is useful because it extracts richer details from images than a basic color comparison would. The creator tried these multiple approaches to show different tradeoffs between speed and accuracy. The spreadsheet method is very fast for scanning through 314,000 entries. The color comparison method is more visual but might miss duplicates that are the same shape but different colors. The deep learning method is the most thorough for truly understanding the image content, though it requires more computing power. The README doesn't go into detail about the final performance comparisons between these methods, but the code is available to explore each technique.