# crossmodal

**Repository Path**: gfdr5/crossmodal

## Basic Information

- **Project Name**: crossmodal
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-01-02
- **Last Updated**: 2026-01-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

This project is a deep learning-based cross-modal retrieval system designed to enable mutual retrieval between images and text. Core functionalities include feature extraction for images and text, similarity computation, and loss function design, supporting multiple backbone networks (e.g., Faster R-CNN, BiGRU, Swin Transformer, etc.). The project structure is clear and modular, facilitating easy extension.

### Key Features
- **Multimodal Data Processing**: Supports loading and preprocessing of image and text data.
- **Flexible Model Architecture**: Integrates various image and text encoders with customizable configurations.
- **Advanced Loss Functions**: Implements multiple optimization strategies, including contrastive loss and triplet loss.
- **Visualization Tools**: Provides features such as feature alignment visualization and heatmap generation.

### Installation Dependencies
```bash
pip install torch torchvision torchaudio
pip install transformers
```

### Usage
1. Prepare your dataset and configure the file paths.
2. Modify parameters in `settings.py` to suit your requirements.
3. Run `main.py` to start training or testing.

### License
This project is licensed under the MIT License.