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).
1from datasets import load_dataset
2from transformers import pipeline
34# Load the tech4humans signature dataset5dataset = load_dataset("samuellimabraz/signature-detection")67# Load the finetuned model8yolos = pipeline(9 task="object-detection",10 model="mdefrance/yolos-tiny-signature-detection",11 device_map="auto",12)1314# Inference on test sample15prediction = 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.
The training utilized a dataset built from two public datasets: Tobacco800 and signatures-xc8up, unified and processed in Roboflow.
The processed dataset was created by Samuel Lima Braz, and all credit for the dataset preparation goes to him.
