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: mlp_noise.k.groupnet.yaml
Vision Backbone: dinov2_vitb14_reg
Language Model: GPT-2
Input Resolution: 518x518
Prefix Size: 768

DeCap Configuration

Memory Bank: Not used
Projection Type: coco
Linear Talk2DINO: False

📊 Performance

Task	METEOR	CIDEr	SPICE
Image Captioning	nan	N/A	nan
Narratives	10.100	38.307	66.297

📈 Detailed Results

Image Captioning Results

METEOR: nan
SPICE: nan
BLEU_4: nan
ROUGE_L: nan

Narratives Results

METEOR: 10.1000
CIDEr: 38.3069
SPICE: 66.2970
BLEU_4: 8.9000
ROUGE_L: 8.6000
CLIP-S: 7.9000

🚀 Quick Start

python

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: 0
Normalization: True

📈 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:

Check the main repository for existing issues
Open a new issue with detailed information about your problem
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

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

Ruggero1912/Patch-ioner_talk2dino_capdec_groupnet_COCO_Captions

Patch-ioner_talk2dino_capdec_groupnet_COCO_Captions - Patch-ioner Configuration

📝 Paper Information