README.md

---
license: apache-2.0
pipeline_tag: zero-shot-image-classification
tags:
- datology
- clip
- vision
- OpenCLIP
- datacomp
- image-text-retrieval
- multimodal
---

# DatologyAI CLIP Retrieval Optimized ViT-B/32

**DatologyAI CLIP Retrieval** is a state-of-the-art contrastive vision-language model optimized for image-text retrieval tasks through advanced data curation. This retrieval-optimized ViT-B/32 model achieves competitive performance with SigLIP2 while requiring significantly less compute.

## Model Description

DatologyAI's retrieval-optimized CLIP model demonstrates superior performance on retrieval benchmarks through targeted data curation strategies:

- **State-of-the-art MSCOCO performance** for ViT-B/32 models
- **2x training efficiency** compared to SigLIP2
- Optimized for text-based distribution alignment
- Standard CLIP architecture with retrieval-focused data curation

## Intended Uses

This model is optimized for image-text retrieval tasks, cross-modal search, and multimodal understanding applications.

### Image-to-Text Retrieval

```python
import torch
from PIL import Image
import open_clip

# Load model and preprocessing
model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:DatologyAI/retr-opt-vit-b-32')
tokenizer = open_clip.get_tokenizer('hf-hub:DatologyAI/retr-opt-vit-b-32')

# Load and process image
image = preprocess(Image.open("path/to/image.jpg")).unsqueeze(0)

# Define text candidates
texts = [
    "a photo of a cat",
    "a dog playing in the park",
    "a beautiful sunset over the ocean",
    "people walking in a city"
]
text_tokens = tokenizer(texts)

# Compute similarities
with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text_tokens)
    
    # Normalize features
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)
    
    # Calculate similarity
    similarity = (100.0 * image_features @ text_features.T)
    
# Get top matches
values, indices = similarity[0].topk(len(texts))
for idx, score in zip(indices, values):
    print(f"{texts[idx]}: {score.item():.2f}")
```

### Text-to-Image Retrieval

```python
import torch
import open_clip
from typing import List

def retrieve_images(query: str, image_features: torch.Tensor, top_k: int = 5):
    """
    Retrieve top-k images for a text query
    
    Args:
        query: Text description to search for
        image_features: Pre-computed normalized image features [N, 512]
        top_k: Number of images to retrieve
    """
    # Encode text query
    text_tokens = tokenizer([query])
    with torch.no_grad():
        text_features = model.encode_text(text_tokens)
        text_features /= text_features.norm(dim=-1, keepdim=True)
    
    # Compute similarities
    similarities = (100.0 * text_features @ image_features.T).squeeze()
    
    # Get top-k matches
    values, indices = similarities.topk(top_k)
    return indices.tolist(), values.tolist()

# Example usage
model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:DatologyAI/retr-opt-vit-b-32')
tokenizer = open_clip.get_tokenizer('hf-hub:DatologyAI/retr-opt-vit-b-32')

# Pre-compute image features for your dataset
# image_features = ... # Shape: [num_images, 512]

# Search for images
indices, scores = retrieve_images("a red sports car", image_features)
```

## Training Procedure

DatologyAI's retrieval-optimized pipeline employs specialized curation techniques:

1. **Text-aligned distribution matching** - Prioritizes alignment along text representations for retrieval tasks
2. **Retrieval-specific synthetic data** - Optimized caption generation for cross-modal understanding  
3. **Balanced multimodal representation** - Ensures strong performance in both directions

The model uses standard CLIP contrastive objectives without architectural modifications.

## Training Data

The model was trained on image-text pairs curated from the **DataComp-XL** dataset using DatologyAI's retrieval-optimized curation pipeline, selecting high-quality pairs that enhance cross-modal alignment.

## Evaluation Results

### Retrieval Performance

| Benchmark | Metric | DatologyAI | SigLIP2 | MetaCLIP |
|-----------|--------|------------|---------|----------|
| **MSCOCO** |  Retrieval@1 | 55.53% | 55.45% | 46.6% | 
| **Flickr30K** | Retrieval@1 | 79.7% | 82.4% | 72.9% | 

### Training Efficiency
- Matches SigLIP2 MSCOCO performance with **50% fewer samples** (20B vs 40B)
- Exceeds MetaCLIP by >5% absolute on both benchmarks

## Model Details

- **Developed by:** DatologyAI
- **Model type:** CLIP (Contrastive Language-Image Pre-training)
- **Architecture:** Vision Transformer B/32
- **License:** Apache 2.0
- **Training framework:** OpenCLIP 2.24.0
- **Optimization focus:** Image-text retrieval

## Technical Specifications

### Model Architecture
- **Vision Encoder:** ViT-B/32 (86M parameters)
  - Patch size: 32×32
  - Image size: 224×224
  - Embedding dimension: 512
- **Text Encoder:** 12-layer Transformer
  - Context length: 77 tokens
  - Vocabulary size: 49,408 (BPE tokenizer)

### Training Configuration
- **Optimizer:** AdamW (β1=0.9, β2=0.98, ε=1e-6)
- **Learning rate:** 1e-3 with cosine schedule
- **Weight decay:** 0.1
- **Batch size:** 32,768
- **Training approach:** Retrieval-optimized data curation
- **Hardware:** Distributed training on H100 GPUs

## Usage Tips

1. **Feature Caching**: For large-scale retrieval, pre-compute and cache image features
2. **Batch Processing**: Process multiple queries simultaneously for efficiency
3. **Normalization**: Always normalize features before computing similarities
4. **Temperature Scaling**: Adjust similarity temperature for different use cases

## Citation

If you use this model, please cite:

```bibtex
@article{datologyai2025clip,
  title={CLIP Gets a Data Upgrade: Outperforming SoTA with Improved Data Curation Only},
  author={DatologyAI Team},
  journal={DatologyAI Blog},
  year={2025},
  url={https://datologyai.com/blog/clip-data-upgrade}
}
```

## Additional Information

For more details on our data curation methodology and comprehensive benchmark results, please visit our [blog post](https://datologyai.com/blog/clip-data-upgrade).

**Contact:** [team@datologyai.com](mailto:team@datologyai.com)

## Model Card Contact

DatologyAI Team - [team@datologyai.com](mailto:team@datologyai.com)