Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- openmmlab/mask-rcnn
+- microsoft/swin-base-patch4-window7-224-in22k
+pipeline_tag: image-segmentation
+---
+
+# Model Card for ChartPointNet-InstanceSeg
+
+ChartPointNet-InstanceSeg is a high-precision data point instance segmentation model for scientific charts. It uses Mask R-CNN with a Swin Transformer backbone to detect and segment individual data points, especially in dense and small-object scenarios common in scientific figures.
+
+## Model Details
+
+### Model Description
+
+ChartPointNet-InstanceSeg is designed for pixel-precise instance segmentation of data points in scientific charts (e.g., scatter plots). It leverages Mask R-CNN with a Swin Transformer backbone, trained on enhanced COCO-style datasets with instance masks for data points. The model is ideal for extracting quantitative data from scientific figures and for downstream chart analysis.
+
+- **Developed by:** Hansheng Zhu
+- **Model type:** Instance Segmentation
+- **License:** Apache-2.0
+- **Finetuned from model:** openmmlab/mask-rcnn
+
+### Model Sources
+
+- **Repository:** [https://github.com/hanszhu/ChartSense](https://github.com/hanszhu/ChartSense)
+- **Paper:** https://arxiv.org/abs/2106.01841
+
+## Uses
+
+### Direct Use
+
+- Instance segmentation of data points in scientific charts
+- Automated extraction of quantitative data from figures
+- Preprocessing for downstream chart understanding and data mining
+
+### Downstream Use
+
+- As a preprocessing step for chart structure parsing or data extraction
+- Integration into document parsing, digital library, or accessibility systems
+
+### Out-of-Scope Use
+
+- Segmentation of non-data-point elements
+- Use on figures outside the supported chart types
+- Medical or legal decision making
+
+## Bias, Risks, and Limitations
+
+- The model is limited to data point segmentation in scientific charts.
+- May not generalize to figures with highly unusual styles or poor image quality.
+- Potential dataset bias: Training data is sourced from scientific literature.
+
+### Recommendations
+
+Users should verify predictions on out-of-domain data and be aware of the model’s limitations regarding chart style and domain.
+
+## How to Get Started with the Model
+
+```python
+import torch
+from mmdet.apis import inference_detector, init_detector
+
+config_file = 'legend_match_swin/mask_rcnn_swin_datapoint.py'
+checkpoint_file = 'chart_datapoint.pth'
+model = init_detector(config_file, checkpoint_file, device='cuda:0')
+
+result = inference_detector(model, 'example_chart.png')
+# result: list of detected masks and class labels
+```
+
+## Training Details
+
+### Training Data
+
+- **Dataset:** Enhanced COCO-style scientific chart dataset with instance masks
+- Data point class with pixel-precise segmentation masks
+- Images and annotations filtered and preprocessed for optimal Swin Transformer performance
+
+### Training Procedure
+
+- Images resized to 1120x672
+- Mask R-CNN with Swin Transformer backbone
+- **Training regime:** fp32
+- **Optimizer:** AdamW
+- **Batch size:** 8
+- **Epochs:** 36
+- **Learning rate:** 1e-4
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+- **Testing Data:** Held-out split from enhanced COCO-style dataset
+- **Factors:** Data point density, image quality
+- **Metrics:** mAP (mean Average Precision), AP50, AP75, per-class AP
+
+### Results
+
+| Category        | mAP   | mAP_50 | mAP_75 | mAP_s | mAP_m | mAP_l |
+|-----------------|-------|--------|--------|-------|-------|-------|
+| data-point      | 0.485 | 0.687  | 0.581  | 0.487 | 0.05  |  nan  |
+
+#### Summary
+
+The model achieves strong mAP for data point segmentation, excelling in dense and small-object scenarios. It is highly effective for scientific figures requiring pixel-level accuracy.
+
+## Environmental Impact
+
+- **Hardware Type:** NVIDIA V100 GPU
+- **Hours used:** 10
+- **Cloud Provider:** Google Cloud
+- **Compute Region:** us-central1
+- **Carbon Emitted:** ~15 kg CO2eq (estimated)
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+- Mask R-CNN with Swin Transformer backbone
+- Instance segmentation head for data point class
+
+### Compute Infrastructure
+
+- **Hardware:** NVIDIA V100 GPU
+- **Software:** PyTorch 1.13, MMDetection 2.x, Python 3.9
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@article{DocFigure2021,
+  title={DocFigure: A Dataset for Scientific Figure Classification},
+  author={S. Afzal, et al.},
+  journal={arXiv preprint arXiv:2106.01841},
+  year={2021}
+}
+```
+
+**APA:**
+
+Afzal, S., et al. (2021). DocFigure: A Dataset for Scientific Figure Classification. arXiv preprint arXiv:2106.01841.
+
+## Glossary
+
+- **Data Point:** An individual visual marker representing a value in a scientific chart (e.g., a dot in a scatter plot)
+
+## More Information
+
+- [DocFigure Paper](https://arxiv.org/abs/2106.01841)
+
+## Model Card Authors
+
+Hansheng Zhu
+
+## Model Card Contact
+
+[email protected]