Patch-ioner_siglip2_base_patch16_512_COCO_Captions

Patch-ioner_siglip2_base_patch16_512_COCO_Captions - Patch-ioner Configuration

This repository contains a pre-trained DECAP model from the Patch-ioner framework for dense image captioning and controllable visual description.

📝 Paper Information

Title: "One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework"
Authors: Lorenzo Bianchi, Giacomo Pacini, Fabio Carrara, Nicola Messina, Giuseppe Amato, Fabrizio Falchi
ArXiv: https://arxiv.org/abs/2510.02898 Project Page: https://paciosoft.com/Patch-ioner/

🎯 Model Overview

• Model Type: DECAP
• Configuration: siglip2_base_patch16_512.k.yaml
• Vision Backbone: siglip2_base_patch16_512
• Language Model: GPT-2
• Input Resolution: 512x512
• Prefix Size: 768

DeCap Configuration

• Memory Bank Size: 500,000 entries
• Projection Type: coco
• Linear Talk2DINO: False

📊 Performance

📈 Detailed Results

Detailed evaluation results will be added here, including:

• Per-task performance metrics
• Comparison with baseline methods
• Ablation study results
• Qualitative examples

🚀 Quick Start

from patch_ioner import load_model, Patchioner

# Load the model
config_path = "config.yaml"
model = load_model(config_path)

# Run inference
image_path = "your_image.jpg"
results = model.forward(image_path)
print(results)

📁 Repository Contents

• config.yaml: Model configuration file
• coco_karpathy-009.pt: Pre-trained model weights
• README.md: This file

🔧 Installation

pip install git+https://github.com/Ruggero1912/Patch-ioner

💡 Usage Examples

Refer to the Patch-ioner repository for updated usage examples.

🎛️ Model Configuration

• Prefix Size: 768
• Memory Bank Size: 500,000 entries
• Normalization: False
• Resize Dimension: 512
• Crop Dimension: 512

📈 Training Details

• Training Dataset: COCO Captions
• Training Epochs: TBD
• Batch Size: TBD
• Learning Rate: TBD
• Optimizer: AdamW

📚 Citation

If you use this model in your research, please cite our paper, refer to the Project Page for updated citation template.

🤝 Contributing

We welcome contributions to improve the Patch-ioner framework. Please see the main repository for contribution guidelines.

📄 License

See the main repository for detailed license information.

🐛 Issues and Support

For issues related to this model or the Patch-ioner framework, please:

1. Check the main repository for existing issues
2. Open a new issue with detailed information about your problem
3. Contact the authors.

🔗 Related Models

Explore other Patch-ioner model configurations:

• Patch-ioner_mlp - MLP-based DeCap model
• Patch-ioner_viecap - VieCap controllable captioning
• Patch-ioner_clipcap - ClipCap integration

More models available in Ruggero1912's models

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This model is part of the Patch-ioner framework for dense image captioning and controllable visual description.