This test task aims to evaluate your grasp of fundamental machine learning and development concepts. Your task involves working with an image dataset to develop a system capable of searching for similar images based on textual queries.
Please choose whatever image dataset you prefer, for example:
- crawl some e-commerce website
- find something on Kaggle
- use prepared
Advertisement Image Dataset
#!/bin/bash
wget https://storage.googleapis.com/ads-dataset/subfolder-0.zip
wget https://storage.googleapis.com/ads-dataset/subfolder-1.zip
- Provide summarized report of the dataset contents
- Implement a service, capable of retrieving relevant images based on the text input provided.
- For example: Text query "black cat" should return a picture of a black cat if it is present in the dataset
Tips:
- Use vector search with Qdrant
- Provide solution in a form of FastAPI service or streamlit application
- Provide a set of example queries that effectively showcase the capabilities of your implemented system. These queries should yield relevant images, demonstrating the accuracy and efficiency of your text2image search solution.
- Provide examples of queries that do not perform well, accompanied by explanations outlining the shortcomings of the system.
- Suggest a method of quantitive evaluation of retrieval accuracy. (e.g. how to label dataset and prepare queries?)
- Upload your solution to your GitHub account, accompanied by clear instructions for running the text2image search system.
- Include a
README.mddescribing the system's architecture and techniques employed, challenges encountered during the implementation, and potential avenues for improvement.
Done and shared