clip-object-detection
🚀 CLIP Zero-Shot Object Detection
Detect objects in images without training!
Welcome to the CLIP Zero-Shot Object Detection project! This repository demonstrates how to perform zero-shot object detection by integrating OpenAI’s CLIP (Contrastive Language-Image Pretraining) model with a Faster R-CNN for region proposal generation.
| Source Code | Website |
|---|---|
| github.com/deepmancer/clip-object-detection | deepmancer.github.io/clip-object-detection |
🎯 Quick Start
Set up and run the pipeline in three simple steps:
-
Clone the Repository:
git clone https://github.com/deepmancer/clip-object-detection.git cd clip-object-detection -
Install Dependencies:
pip install -r requirements.txt -
Run the Notebook:
jupyter notebook clip_object_detection.ipynb
🤔 What is CLIP?
CLIP (Contrastive Language–Image Pretraining) is trained on 400 million image-text pairs. It embeds images and text into a shared space where the cosine similarity between embeddings reflects their semantic relationship.
🔍 Methodology
Our approach combines CLIP and Faster R-CNN for zero-shot object detection:
- 📦 Region Proposal: Use Faster R-CNN to identify potential object locations.
- 🎯 CLIP Embeddings: Encode image regions and text descriptions into a shared embedding space.
- 🔍 Similarity Matching: Compute cosine similarity between text and image embeddings to identify matches.
- ✨ Results: Highlight detected objects with their confidence scores.
📊 Example Results
Input Image
Region Proposals
Regions proposed by Faster R-CNN’s RPN:
Detected Objects
Objects detected by CLIP based on textual queries:
📦 Requirements
Ensure the following are installed:
- PyTorch: Deep learning framework.
- Torchvision: Pre-trained Faster R-CNN.
- OpenAI CLIP: GitHub Repository.
- Additional dependencies are listed in requirements.txt.
📝 License
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code.
⭐ Support the Project
If this project inspires or assists your work, please consider giving it a ⭐ on GitHub! Your support motivates us to continue improving and expanding this repository.