mdefrance/yolos-tiny-signature-detection

YOLOS (tiny-sized) Model For Handwritten Signature Detection

YOLOS model fine-tuned to detect handwritten signatures in document images using tech4humans/signature-detection dataset.

Original YOLOS was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository.

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).

The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

Direct Use

Here is how to use this model:

from transformers import YolosImageProcessor, YolosForObjectDetection
from PIL import Image
import torch
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

model = YolosForObjectDetection.from_pretrained('mdefrance/yolos-tiny-signature-detection')
image_processor = YolosImageProcessor.from_pretrained("mdefrance/yolos-tiny-signature-detection")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)

# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
bboxes = outputs.pred_boxes


# print results
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(
        f"Detected Signature with confidence "
        f"{round(score.item(), 3)} at location {box}"
    )

Currently, both the feature extractor and model support PyTorch.

Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

The training utilized a dataset built from two public datasets: Tobacco800 and signatures-xc8up, unified and processed in Roboflow.

Dataset Summary:

• Training: 1,980 images (70%)
• Validation: 420 images (15%)
• Testing: 419 images (15%)
• Format: COCO JSON
• Resolution: 640x640 pixels

Training Procedure

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

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