FiCo-ITR: Fine-grained and Coarse-grained Image-Text Retrieval

A unified evaluation library for image-text retrieval research

Overview

FiCo-ITR provides a standardized evaluation framework for image-text retrieval models, supporting both instance-level (fine-grained) and category-level (coarse-grained) retrieval evaluation. The library handles diverse data formats encountered in current research, making it easy to evaluate and compare different models.

✨ Features

• Universal Format Support - Automatically handles various embedding and matrix formats and orientations
• Flexible Distributions - Supports both uniform and non-uniform caption distributions
• Dual Evaluation - Instance-level and category-level retrieval tasks and metrics
• Zero Configuration - Works out-of-the-box for most models
• Extensible - Easy to adapt for custom evaluation needs

📦 Installation

pip install fico_itr

🚀 Quick Start

from fico_itr import instance_retrieval, category_retrieval

# Compute Similarity Matrix
similarity_matrix = compute_similarity(image_embeddings, text_embeddings, measure='cosine')

# Instance-level retrieval - just pass your similarity matrix
i2t_instance_results, t2i_instance_results = instance_retrieval(similarity_matrix)

# Category-level retrieval - add labels for mAP evaluation
i2t_category_results, t2i_category_results = category_retrieval(similarity_matrix, labels)

print(f"Instance Retrieval Results - I2T: {i2t_instance_results} T2I: {t2i_instance_results}")
print(f"Category Retrieval Results - I2T: {i2t_category_results} T2I: {t2i_category_results}")

📖 Usage Guide

1. Standard Case (Uniform Images x Captions Matrix)

# Just pass your similarity matrix
results = instance_retrieval(similarity_matrix)

2. Non-uniform Caption Distribution

For datasets where images have varying numbers of captions (e.g., COCO with 25010 captions):

# Option A: Load pre-computed mapping (for COCO)
caption_mapping = np.load('mscoco_test_indices.npy')

# Option B: Create your own mapping
caption_mapping = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, ...]  # Which image each caption belongs to

results = instance_retrieval(similarity_matrix, captions_per_image=caption_mapping)

3. Square Matrices (Duplicated Images)

Some models duplicate images to create square matrices:

# E.g., vsrn/ucch with 5000×5000 matrix (1000 images duplicated 5×)
results = instance_retrieval(similarity_matrix, captions_per_image=5)

4. Separate Directional Matrices

For models with different similarity matrices per direction:

# E.g., BLIP2, XVLM with task-specific fine-tuning
results = instance_retrieval(
    i2t_similarity_matrix,
    t2i_sim=t2i_similarity_matrix,
    captions_per_image=5  # or caption_mapping for non-uniform
)

📊 Model Examples

📚 Documentation

• Technical Documentation - Implementation details
  • Alignment - How data alignment works
  • Similarity - Available similarity measures
  • Tasks - Evaluation metrics and algorithms
• Paper Tutorial - Reproduce paper results with pre-computed embeddings

📋 Requirements

• Python 3.7+
• numpy

📄 Citation

If you use FiCo-ITR in your research, please cite:

@article{williams-lekuona2025ficoitr,
  author  = {Mikel Williams-Lekuona and Georgina Cosma},
  title   = {FiCo-ITR: Bridging Fine-Grained and Coarse-Grained Image-Text Retrieval for Comparative Performance Analysis},
  journal = {International Journal of Multimedia Information Retrieval},
  volume  = {14},
  number  = {2},
  pages   = {20},
  year    = {2025},
  publisher={Springer}
}

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

This project includes code generated with the assistance of AI coding tools.