t29mato/lineformer-battery-finetuned

LineFormer Fine-tuned on Battery Discharge Curves

This model is a fine-tuned version of LineFormer specialized for detecting and segmenting line charts in battery charge/discharge curve figures from scientific papers.

Model Description

LineFormer is an instance segmentation model designed to detect individual lines in scientific plots. This fine-tuned version has been optimized for battery electrochemistry figures, particularly charge/discharge curves with multiple C-rates.

Base Model: LineFormer (pre-trained on scientific figure datasets) Fine-tuning Dataset: 62 battery charge/discharge curve images from scientific papers Validation Dataset: 19 battery curve images Framework: MMDetection (Mask R-CNN based)

Performance

Evaluated using the original LineFormer evaluation methodology (ICDAR 2023 Task 6a/6b), which matches predicted lines to ground truth lines using linear interpolation and the Hungarian algorithm.

Task 6a / 6b Scores (Original Paper Metrics)

Model	Task 6a	Task 6b	GT Lines	Detected	Over-detection
Pre-trained	0.9471	0.6835	146	237	+62.3%
Fine-tuned Best (iter_1300)	0.9180	0.7394	146	179	+22.6%
Fine-tuned Final (iter_5000)	0.9097	0.7836	146	160	+9.6%

• Task 6a: Measures how well each GT line is matched (no penalty for extra detections)
• Task 6b: Penalizes over-detection — the more practical metric

Key improvements (Pre-trained → Fine-tuned iter_5000):

• Task 6b (practical metric): 0.6835 → 0.7836 (+14.6%)
• Over-detection reduced from +62.3% to +9.6%
• Reduced false positives from text annotations, legends, and axis labels
• Better handling of densely packed multi-cycle curves

Additional Metrics

Model	segm_mAP	Confidence Score
Pre-trained	0.0395	0.791
Fine-tuned Best (iter_1300)	0.1587	0.872
Fine-tuned Final (iter_5000)	0.1451	0.896

Usage

Requirements

pip install mmdet mmcv torch torchvision

Inference

from mmdet.apis import init_detector, inference_detector
import mmcv

# Load model
config_file = 'configs/battery_finetune.py'
checkpoint_file = 'lineformer_battery_iter_5000.pth'
model = init_detector(config_file, checkpoint_file, device='cuda:0')

# Inference
img = mmcv.imread('battery_curve.png')
result = inference_detector(model, img)

# Visualize
model.show_result(img, result, out_file='result.jpg', score_thr=0.5)

Model Files

We provide two checkpoints with different strengths:

Model	Task 6b	segm_mAP	Over-detection	Best For
lineformer_battery_iter_5000.pth	0.7836	0.1451	+9.6%	Data extraction accuracy (recommended)
lineformer_battery_best_iter_1300.pth	0.7394	0.1587	+22.6%	Mask shape precision

Which model to use?

• For most use cases: Use lineformer_battery_iter_5000.pth — highest Task 6b score (0.7836) with fewest false positives
• For mask segmentation tasks: Use lineformer_battery_best_iter_1300.pth if you need precise mask boundaries (higher mAP)

Training Details

Training Hyperparameters

• Optimizer: SGD (lr=0.0001, momentum=0.9, weight_decay=0.0001)
• Learning Rate Schedule: Step decay at iterations [3500, 4750]
• Total Iterations: 5000
• Batch Size: 2
• Image Size: Resized to shorter edge 800px
• Data Augmentation: RandomFlip (flip_ratio=0.5)
• Backbone: ResNet-50 (frozen, pre-trained on ImageNet)

Training Data

• Train: 62 images (battery charge/discharge curves from scientific papers)
• Validation: 19 images
• Line Width: 10px (for mask generation)
• Format: COCO instance segmentation format

Training Metrics

Iteration	mAP	mAP_50	mAP_75
500	0.1156	0.2852	0.0005
1300	0.1587	0.3705	0.0006
5000	0.1451	0.3503	0.0004

Note: mAP_75 ≈ 0 indicates that mask boundary precision needs improvement, though line detection count is accurate.

Limitations and Challenges

The model still struggles with:

1. Dense multi-cycle curves (e.g., 30-cycle plots): GT=4 lines → Detected=14-17 lines
2. Dashed lines and annotations: Non-line elements (arrows, dimension lines) may be detected
3. Mask IoU precision: Low mAP_75 suggests mask shape accuracy needs improvement
4. GT definition mismatch: Some images count charge/discharge pairs as single lines vs. separate lines

Intended Use

This model is designed for:

• Automated extraction of battery performance data from scientific literature
• Digitization of charge/discharge curves for meta-analysis
• Pre-processing battery electrochemistry figures for data mining

Not recommended for:

• General line chart detection (use original LineFormer)
• Real-time applications requiring <100ms inference
• Figures with extreme aspect ratios or unconventional layouts

Citation

If you use this model, please cite the original LineFormer paper:

@inproceedings{Xia_2022_WACV,
  title={LineFormer: Rethinking Line Chart Data Extraction as Instance Segmentation},
  author={Xia, Weixin and Lo, Kelvin and Chao, Qing and Li, Tong and Zhao, Jian},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2022}
}

License

Apache 2.0 (same as base LineFormer model)

Model Card Authors

Fine-tuning and evaluation by t29mato

Model Card Contact

For questions about this fine-tuned model, please open an issue in the repository.