签名检测数据集

This dataset focuses on detecting human written signatures within documents. It includes a variety of document types with annotated signatures, providing valuable insights for applications in document verification and fraud detection. Essential for training computer vision algorithms, this dataset aids in identifying signatures in various document formats, supporting research and practical applications in document analysis.

数据集结构

签名检测数据集分为三个子集：

• 训练集：包含 143 幅图像，每幅图像都有相应的注释。
• 验证集：包括 35 幅图像，每幅图像都有配对注释。

应用

该数据集可用于各种计算机视觉任务，如物体检测、物体跟踪和文档分析。具体来说，它可用于训练和评估识别文档中签名的模型，这些模型可应用于文档验证、欺诈检测和档案研究。此外，它还可以作为宝贵的教育资源，使学生和研究人员能够研究和了解不同文档类型中签名的特征和行为。

数据集 YAML

YAML（另一种标记语言）文件定义了数据集配置，包括路径和类信息。对于签名检测数据集，YAML 文件中的 signature.yaml 文件位于 https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml.

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Signature dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/signature/
# Example usage: yolo train data=signature.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── signature  ← downloads here (11.2 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/signature # dataset root dir
train: train/images # train images (relative to 'path') 143 images
val: valid/images # val images (relative to 'path') 35 images

# Classes
names:
  0: signature

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/signature.zip

使用方法

To train a YOLO11n model on the signature detection dataset for 100 epochs with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="signature.yaml", epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640

from ultralytics import YOLO

# Load a model
model = YOLO("path/to/best.pt")  # load a signature-detection fine-tuned model

# Inference using the model
results = model.predict("https://ultralytics.com/assets/signature-s.mp4", conf=0.75)

# Start prediction with a finetuned *.pt model
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/signature-s.mp4" conf=0.75

图片和注释示例

签名检测数据集包含各种图像，展示了不同的文件类型和带注释的签名。以下是数据集中的图像示例，每张图像都附有相应的注释。

• 镶嵌图像在这里，我们展示了由镶嵌数据集图像组成的训练批次。马赛克是一种训练技术，可将多幅图像合成一幅，从而丰富批次的多样性。这种方法有助于增强模型在不同特征尺寸、长宽比和背景下的泛化能力。

这个例子说明了签名检测数据集中图像的多样性和复杂性，强调了在训练过程中使用马赛克的好处。

引文和致谢

该数据集根据AGPL-3.0 许可发布。

常见问题

什么是签名检测数据集，如何使用？

The Signature Detection Dataset is a collection of annotated images aimed at detecting human signatures within various document types. It can be applied in computer vision tasks such as object detection and tracking, primarily for document verification, fraud detection, and archival research. This dataset helps train models to recognize signatures in different contexts, making it valuable for both research and practical applications.

How do I train a YOLO11n model on the Signature Detection Dataset?

To train a YOLO11n model on the Signature Detection Dataset, follow these steps:

1. 下载 signature.yaml 数据集配置文件 signature.yaml.
2. 使用以下Python 脚本或CLI 命令开始训练：

from ultralytics import YOLO

# Load a pretrained model
model = YOLO("yolo11n.pt")

# Train the model
results = model.train(data="signature.yaml", epochs=100, imgsz=640)

yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640

有关详细信息，请参阅培训页面。

签名检测数据集的主要应用是什么？

签名检测数据集可用于：

1. 文件验证：自动验证文件中是否有人签名以及签名的真实性。
2. 欺诈检测：识别法律和财务文件中的伪造或欺诈性签名。
3. 档案研究：协助历史学家和档案管理人员对历史文献进行数字分析和编目。
4. Education: Supporting academic research and teaching in the fields of computer vision and machine learning.

如何使用在签名检测数据集上训练的模型进行推理？

要使用在签名检测数据集上训练的模型进行推理，请按照以下步骤操作：

1. 加载微调后的模型。
2. 使用以下Python 脚本或CLI 命令执行推理：

from ultralytics import YOLO

# Load the fine-tuned model
model = YOLO("path/to/best.pt")

# Perform inference
results = model.predict("https://ultralytics.com/assets/signature-s.mp4", conf=0.75)

yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/signature-s.mp4" conf=0.75

签名检测数据集的结构是什么？

签名检测数据集分为两个子集：

• 训练集：包含 143 幅带注释的图像。
• 验证集：包括 35 幅带注释的图像。

有关详细信息，请参阅 数据集结构 部分。此外，还可在 signature.yaml 位于 signature.yaml.

📅 Created 5 months ago ✏️ Updated 23 days ago

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