Model Card for ChartDet

Model Details

Model Description

ChartDet is an implementation of the Swin Transformer block used by the ChartEye model, adapted for chart classification tasks. While the ChartEye paper focuses on identifying specific chart types, this model is trained to distinguish between charts and non-chart images.

Developed by: Stefano D’Angelo
Model type: Image Classification (Swin Transformer)
Language(s) (NLP): Not applicable
License: MIT
Finetuned from model : microsoft/swin-large-patch4-window7-224

Model Sources

Repository: ChartDet GitHub Repository
Paper : ChartEye Paper

Uses

Direct Use

This model can be used to classify images into chart and non-chart categories directly.

Downstream Use

The model can be fine-tuned further for specific chart type classification tasks or integrated into applications for automated document analysis.

Out-of-Scope Use

The model is not designed for identifying specific chart types (e.g., bar, line, pie) or for tasks outside chart detection.

Bias, Risks, and Limitations

The model was trained on datasets like ICPR2022 CHARTINFO, PACS, and DomainNet, which may not fully represent all chart types or images in real-world scenarios. Potential biases include:

Dataset Bias: The training datasets may underrepresent certain chart styles or image types, impacting model generalization.
Domain Limitations: Performance may degrade on charts or images from unseen domains or with significant visual noise.
Misclassification Risk: Non-chart images with chart-like features (e.g., diagrams) may occasionally be misclassified.

Users should carefully evaluate the model on their specific data to ensure compatibility and adjust as needed.

Recommendations

Users should ensure that input data matches the training domain to achieve optimal performance.
Avoid using the model for unrelated image classification tasks without fine-tuning.

How to Get Started with the Model

Use the following code snippet to get started:

Coming soon

Training Details

Training Data

The model was trained on a combination of:

Training Procedure

The model was fine-tuned using the following setup:

Preprocessing

Images were preprocessed to match the input requirements of the Swin Transformer model (e.g., resizing, normalization).

Training Hyperparameters

Optimizer: Adam
Loss Function: CrossEntropyLoss
Batch Size: 8
Epochs: 12
Learning Rate: 3e-6
Seed: 42

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluation used subsets of the datasets mentioned above, with metrics computed on held-out validation or test splits on an 80-20 split strategy.

Factors

Performance was assessed across images from diverse domains (e.g., charts vs. natural images).

Metrics

Evaluation metrics included:

Accuracy
Confusion Matrix
Classification Report (e.g., Precision, Recall, F1-Score)

Results

Results indicate effective performance in distinguishing between charts and non-chart images. Quantitative results are as follows:

Accuracy: 99.89%
Precision (Weighted): 99.80%
Recall (Weighted): 99.93%
F1-Score (Weighted): 99.87%

Summary

The model achieves reliable classification for the intended task within the training domain.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: NVIDIA GeForce RTX 4070 Ti
Hours used: ~2.47 hours (total training time: 12 epochs x ~740 seconds per epoch = ~8880 seconds)
Cloud Provider: Local (no cloud provider used)
Compute Region: Not applicable (local machine)
Carbon Emitted: Not computed (local compute environment without data on power source or emissions factor)

Technical Specifications

Model Architecture and Objective

The model uses a Swin Transformer-based architecture adapted for binary image classification.

Compute Infrastructure

Hardware

NVIDIA GeForce RTX 4070 Ti

Software

Windows 11
Python 3.11
HuggingFace Transformers Library
PyTorch

Citation

BibTeX:

@misc{chartdet2025,
  author = {Stefano D’Angelo},
  title = {ChartDet: A Swin Transformer Model for Chart Classification},
  year = {2025},
  howpublished = {\url{https://huggingface.co/stefanodangelo/chartdet}}
}

Model Card Authors

Stefano D’Angelo

stefanodangelo
/

chartdet