---
library_name: transformers.js
license: apache-2.0
datasets:
- tech4humans/signature-detection
base_model:
- mdefrance/yolos-base-signature-detection
pipeline_tag: object-detection
---
# yolos-base-signature-detection (ONNX)
This is an ONNX version of [mdefrance/yolos-base-signature-detection](https://huggingface.co/mdefrance/yolos-base-signature-detection). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
## Usage with Transformers.js
See the pipeline documentation for `object-detection`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ObjectDetectionPipeline
---
# YOLOS (base-sized) Model For Handwritten Signature Detection
YOLOS model finetuned to detect handwritten signatures in document images using [[tech4humans/signature-detection](https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md.svg)](https://huggingface.co/datasets/tech4humans/signature-detection) dataset.
Original YOLOS was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS).
## Model description
YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
- **Finetuned by:** [Mario DEFRANCE](www.linkedin.com/in/mario-defrance)
- **Repository:** [mdefrance/signature-detection](https://github.com/mdefrance/signature-detection/)
- **Model type:** [YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)
- **License:** Apache 2.0 license
- **Finetuned from model** [hustvl/yolos-base](https://huggingface.co/hustvl/yolos-base)
## Uses
This model is designed for detecting handwritten signatures in scanned documents, contracts, or forms.
You can try it instantly in your browser here:
[](https://huggingface.co/spaces/mdefrance/signature-detection-demo)
### Direct Use
Here is how to use this model:
```python
from datasets import load_dataset
from transformers import pipeline
# Load the tech4humans signature dataset
dataset = load_dataset("samuellimabraz/signature-detection")
# Load the finetuned model
yolos = pipeline(
task="object-detection",
model="mdefrance/yolos-base-signature-detection",
device_map="auto",
)
# Inference on test sample
prediction = yolos(dataset["test"][0].get("image"))
```
Currently, both the image processor and model support PyTorch.
### Out-of-Scope Use
- **Fraudulent Use:** This model must not be used for forging signatures or any illegal activity. It’s meant for legitimate signature detection in documents.
- **Other Objects:** Not suitable for detecting non-signature elements in documents.
- **Critical Decisions:** Should not be solely relied on for high-stakes decisions (e.g., legal or financial) without human validation.
## Bias, Risks, and Limitations
- **Bias:** May not generalize well if training data lacks diversity in signature styles or cultural context.
- **Risks:** False positives/negatives can occur, impacting document validation.
- **Limitations:** Performance may degrade on poor-quality images or in challenging visual conditions (e.g., noise, lighting).
### Recommendations
- **Improve Training Data:** Fine-tune with diverse and representative samples to reduce bias.
- **Human Oversight:** Always include a human review step for critical use cases.
- **Image Quality:** Use clean, high-resolution images; apply preprocessing if needed.
- **Ethical Use:** Follow legal and ethical standards, ensuring privacy and responsible deployment.
## Training Details
### Training Data
The training utilized a dataset built from two public datasets: [Tobacco800](https://paperswithcode.com/dataset/tobacco-800) and [signatures-xc8up](https://universe.roboflow.com/roboflow-100/signatures-xc8up), unified and processed in [Roboflow](https://roboflow.com/).
The processed dataset was created by [Samuel Lima Braz](https://huggingface.co/samuellimabraz), and all credit for the dataset preparation goes to him.
**Dataset Summary:**
- Training: 1,980 images (70%)
- Validation: 420 images (15%)
- Testing: 419 images (15%)
- Format: COCO JSON
- Resolution: 640x640 pixels
### Training Procedure
See [mdefrance/signature-detection](https://github.com/mdefrance/signature-detection/) for details on training procedure.
#### Metrics
Performances computed on the testing set:
| **Metric** | [yolos-base-signature-detection](https://huggingface.co/mdefrance/yolos-base-signature-detection) | [yolos-small-signature-detection](https://huggingface.co/mdefrance/yolos-small-signature-detection) | [yolos-tiny-signature-detection](https://huggingface.co/mdefrance/yolos-tiny-signature-detection) |
|:--------------------------------|------------:|-----------:|-----------------------------:|
| **Inference Time - CPU (s)** | 2.250 | 0.787 | **0.262** |
| **Inference Time - GPU (s)** | 1.464 | 0.023 | **0.014** |
| **Parameters** | 127.73M | 30.65M | 6.47M |
| **mAP50** | **0.887** | 0.859 | 0.856 |
| **mAP50-95** | **0.495** | 0.419 | 0.395 |
Inference times are computed on a laptop with following specs:
* CPU: Intel Core i7-9750H
* GPU: NVIDIA GeForce GTX 1650
## License Comparison
### GNU Affero General Public License v3.0 (AGPL-3.0)
AGPL-3.0 is a strong copyleft license designed to keep software and its modifications open-source, especially for web apps and network services.
- **Strong Copyleft**: Modified versions must also be AGPL-licensed.
- **Network Use**: Users must get the source code, even if they only interact with the software over a network.
- **Commercial Use**: Allowed, but any changes must be shared under AGPL-3.0.
- **Patent Protection**: Includes safeguards against patent and trademark claims.
### Apache License 2.0
Apache 2.0 is a permissive license that offers flexibility for both open-source and proprietary use.
- **Permissive**: Modifications and derivatives don’t need to be open-source.
- **Commercial Use**: Fully allowed with no requirement to share changes.
- **Patent Protection**: Includes strong patent clauses.
- **Compatibility**: Easy to combine with other licenses and projects.
### Summary: Why Apache 2.0 Offers More Flexibility
While AGPL-3.0 ensures openness, Apache 2.0 is better suited for businesses and closed-source use:
- No obligation to disclose modified code.
- Easier to integrate into proprietary systems.
- More flexible for commercial applications.
For full license texts, see:
- [GNU AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html)
- [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Citation
This model is a finetuned version of the YOLOS model introduced in the following paper. If you use this model, please cite the original work:
**BibTeX:**
```bibtex
@article{DBLP:journals/corr/abs-2106-00666,
author = {Yuxin Fang and
Bencheng Liao and
Xinggang Wang and
Jiemin Fang and
Jiyang Qi and
Rui Wu and
Jianwei Niu and
Wenyu Liu},
title = {You Only Look at One Sequence: Rethinking Transformer in Vision through
Object Detection},
journal = {CoRR},
volume = {abs/2106.00666},
year = {2021},
url = {https://arxiv.org/abs/2106.00666},
eprinttype = {arXiv},
eprint = {2106.00666},
timestamp = {Fri, 29 Apr 2022 19:49:16 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Additional Resources
- **Blog post of comparison of Signature Detection Models:** [Hugging Face Blog](https://huggingface.co/blog/samuellimabraz/signature-detection-model)
- **Blog post associated Finetuning Notebook:** [Google Colab Notebook](https://colab.research.google.com/drive/1wSySw_zwyuv6XSaGmkngI4dwbj-hR4ix)
- **Finetuning of YOLOS Notebook Example:** [Google Colab Notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/YOLOS/Fine_tuning_YOLOS_for_object_detection_on_custom_dataset_(balloon).ipynb)
## Acknowledgements
This model is finetuned for handwritten signature detection using the [[tech4humans/signature-detection](https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md.svg)](https://huggingface.co/datasets/tech4humans/signature-detection) Dataset. The finetuning process and additional resources can be found in the GitHub Repository [mdefrance/signature-detection](https://github.com/mdefrance/signature-detection/).
## **Author**
