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README.md

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-# LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model
+# 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
+
+---
+
+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.
+