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clip-object-detection

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Zero-shot object detection with CLIP, utilizing Faster R-CNN for region proposals.

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# 🚀 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** |
|:-----------------|:------------|
| <a href="https://github.com/deepmancer/clip-object-detection" target="_blank">github.com/deepmancer/clip-object-detection</a> | <a href="https://deepmancer.github.io/clip-object-detection/" target="_blank">deepmancer.github.io/clip-object-detection</a> |

---

## 🎯 Quick Start

Set up and run the pipeline in three simple steps:

1. **Clone the Repository**:

```bash
   git clone https://github.com/deepmancer/clip-object-detection.git
   cd clip-object-detection
   ```

2. **Install Dependencies**:
  
   ```bash
   pip install -r requirements.txt
   ```

3. **Run the Notebook**:

```bash
   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.

<div align="center">
    <figure>
        <img 
            src="https://raw.githubusercontent.com/deepmancer/clip-object-detection/main/assets/CLIP.png" 
            width="600" 
            alt="CLIP Model Architecture"
        />
        <figcaption>
            CLIP Model Architecture - <a href="https://arxiv.org/abs/2103.00020">Paper</a>
        </figcaption>
    </figure>
</div>

---

## 🔍 Methodology

Our approach combines CLIP and Faster R-CNN for zero-shot object detection:

1. **📦 Region Proposal**: Use Faster R-CNN to identify potential object locations.
2. **🎯 CLIP Embeddings**: Encode image regions and text descriptions into a shared embedding space.
3. **🔍 Similarity Matching**: Compute cosine similarity between text and image embeddings to identify matches.
4. **✨ 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](https://github.com/openai/CLIP.git).
- Additional dependencies are listed in [requirements.txt](requirements.txt).

---

## 📝 License

This project is licensed under the [MIT License](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.

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