In this episode, we will explore how Faire tackled the challenge of product categorization. They initially used the K-nearest neighbor algorithm with CLIP embeddings, which improved categorization but still required manual corrections. To further enhance accuracy, the team fine-tuned a vision-language model using their in-house dataset, increasing accuracy significantly. This solution showcases how advanced machine learning can drive business efficiency.

For more details, you can refer to their published tech blog, linked here for your reference: https://craft.faire.com/advancing-product-categorization-with-vision-language-models-the-power-of-fine-tuned-llava-2f4bf024a102