

Clip Image Search
Overview :
clip-image-search is an image search tool based on Open AI's pretrained CLIP model, capable of retrieving images through text or image queries. The CLIP model maps images and text into the same latent space through training, enabling comparison via similarity metrics. This tool utilizes images from the Unsplash dataset and performs k-nearest neighbor searches using Amazon Elasticsearch Service, with query services deployed via AWS Lambda functions and API Gateway, and the frontend developed using Streamlit.
Target Users :
The target audience comprises developers and researchers who need to perform image searches, particularly those interested in image retrieval based on deep learning models. This product is ideal for them as it offers a simple and efficient method for image retrieval and can be easily integrated into existing systems.
Use Cases
Researchers use this tool to retrieve images matching specific text descriptions for visual recognition studies.
Developers integrate this tool into their applications to provide text-based image search capabilities.
Educators use this tool to help students understand the connections between images and text.
Features
Use the CLIP model's image encoder to calculate feature vectors for images in the dataset
Index images by image ID, storing their URLs and feature vectors
Compute feature vectors based on queries (text or image)
Calculate cosine similarity between the query feature vector and dataset image feature vectors
Return the k most similar images
How to Use
Install dependencies
Download the Unsplash dataset and extract metadata
Create an index and upload image feature vectors to Elasticsearch
Build a Docker image for AWS Lambda
Run the Docker image as a container and test it with POST requests
Run a Streamlit application for frontend presentation
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