This repository contains a PyTorch implementation of OpenAI's CLIP model for tasks such as image classification, visual search, and visual question answering (VQA). The repository is currently a work in progress.
CLIP (Contrastive Language-Image Pre-training) is a powerful model developed by OpenAI that can understand images and text in a joint embedding space. This project aims to provide scripts and examples for fine-tuning CLIP on custom datasets for various tasks.
- Image Classification: Fine-tune CLIP for classifying images into custom categories.
- Visual Search: Implement visual search functionality by leveraging CLIP's image and text embeddings.
- Visual Question Answering (VQA): Extend CLIP to answer questions about images.
Instructions and scripts for fine-tuning CLIP on an image classification task will be provided here.
Instructions and scripts for implementing visual search using CLIP will be provided here.
Instructions and scripts for setting up VQA with CLIP will be provided here.
Contributions are welcome! Please open an issue or submit a pull request if you have suggestions or improvements.
This project uses the CLIP model developed by OpenAI. The original CLIP repository can be found here.