---
license: apache-2.0
base_model:
- liuhaotian/llava-v1.5-7b
---


# LISA++ (LISA_Plus_7b): An Improved Baseline for Reasoning Segmentation with Large Language Model


🤗[Data](https://huggingface.co./collections/Senqiao/lisa-67713837a32d6abf516a162e) | 📄[Paper](https://arxiv.org/abs/2312.17240)

# Model Card for LISA++ (LISA_Plus_7b)

## Model Details

- **Developed by**: Senqiao Yang, The Chinese University of Hong Kong & SmartMore  
- **Model Type**: Large Vision-Language Model (VLM) for reasoning segmentation  
- **Language(s)**: Supports natural language queries in English  
- **License**: Apache 2.0  
- **Base Model**: Finetuned from [liuhaotian/llava-v1.5-7b](https://huggingface.co./liuhaotian/llava-v1.5-7b)  

## Model Description

LISA++ (LISA_Plus_7b) is an improved baseline for reasoning segmentation with large language models. It enhances the capabilities of its predecessor by incorporating instance segmentation and enabling more natural, multi-turn dialogues through Segmentation in Dialogue (SiD). These advancements are achieved without structural changes or additional data sources, relying instead on curated samples from existing segmentation datasets.

### Key Enhancements:

1. **Instance Segmentation**: Differentiates between different instances of the same category, providing more detailed scene analysis alongside existing multi-region semantic segmentation.
2. **Segmentation in Dialogue (SiD)**: Improved capability for multi-turn dialogue, allowing the model to incorporate segmentation results directly into text responses, leading to more natural and flexible conversations.
3. **Refined Data Curation**: Uses datasets like COCO and ADE20K to improve segmentation and dialogue integration.

## Intended Uses & Limitations

### Direct Use
- Interactive image understanding and segmentation  
- Multi-turn reasoning about segmented objects in images  
- Visual question-answering with spatial awareness  

### Out-of-Scope Use
- Real-time medical or security applications without further validation  
- Applications requiring precise 3D object segmentation  

## How to Use

As of now, the model is not available via the Hugging Face Inference API. To use locally:

```python
from transformers import pipeline

# Load LISA++
model = pipeline("image-segmentation", model="LISA_Plus_7b")

# Example usage
image_path = "example.jpg"
query = "Highlight all the cats in the image."
result = model(image_path, query)
print(result)
```

For further details, refer to the [model repository](https://huggingface.co./Senqiao/LISA_Plus_7b).

## Training Data

LISA++ is trained on curated samples from:

- **COCO Dataset**: Common Objects in Context  
- **ADE20K Dataset**: Scene parsing dataset  
- **Extended ReasonSeg Dataset**: Enhanced for multi-target instance segmentation  

The training data is structured to improve segmentation and dialogue capabilities.

## Training Procedure

- **Base Model**: Finetuned from [liuhaotian/llava-v1.5-7b](https://huggingface.co./liuhaotian/llava-v1.5-7b)  
- **Optimizer**: [Specify optimizer, e.g., AdamW]  
- **Training Steps**: Trained on ReasonSeg-Inst and ReasonSeg-Sem datasets  
- **Hardware**: Trained on GPUs [Specify model, e.g., NVIDIA A100]  
- **Loss Functions**: Combination of segmentation and language modeling losses  

## Evaluation Results

LISA++ significantly improves segmentation accuracy compared to its predecessor:

- **ReasonSeg-Inst (Instance Segmentation Performance)**:
  - AP50: **34.1%** (vs. 13.7% in LISA-7B)
  - AP75: **22.1%** (vs. 6.6% in LISA-7B)
  - mAP: **21.5%** (vs. 7.2% in LISA-7B)

- **ReasonSeg-Sem (Semantic Segmentation Performance)**:
  - gIoU: **64.2%** (vs. 53.6% in LISA)
  - cIoU: **68.1%** (vs. 52.3% in LISA)

These results highlight LISA++'s enhanced capabilities in both instance and semantic segmentation tasks.

## Bias, Risks, and Limitations

- **Bias**: The model's performance is limited by biases in training datasets (COCO, ADE20K).  
- **Limitations**: May struggle with unseen object categories or highly cluttered scenes.  
- **Ethical Considerations**: Users should verify outputs before deploying in critical applications.  

## Environmental Impact

- **Hardware Used**: NVIDIA A100 GPUs (or equivalent)  
- **Training Duration**: [Specify training time, if available]  
- **Estimated Carbon Emissions**: [Estimate, if available]  

## Citation

If you use LISA_Plus_7b in your research, please cite:

```
@article{yang2024lisa++,
  title={LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model},
  author={Senqiao Yang},
  journal={arXiv preprint arXiv:2312.17240},
  year={2024}
}
```

## Contact Information

For questions or feedback, contact:

- **Author**: Senqiao Yang

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This AI generated model card provides an overview of LISA_Plus_7b's capabilities, training methodology, and evaluation metrics, reflecting the latest updates from the Hugging Face model repository and arXiv paper.