Add Signature ✍️ dataset in docs (#12977)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/datasets/detect/index.md

@@ -72,19 +72,21 @@ Here's how you can use these formats to train your model:
 
 Here is a list of the supported datasets and a brief description for each:
 
-- [**Argoverse**](argoverse.md): A collection of sensor data collected from autonomous vehicles. It contains 3D tracking annotations for car objects.
-- [**COCO**](coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
-- [**LVIS**](lvis.md): LVIS is a large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
-- [**COCO8**](coco8.md): A smaller subset of the COCO dataset, COCO8 is more lightweight and faster to train.
-- [**GlobalWheat2020**](globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
-- [**Objects365**](objects365.md): A large-scale object detection dataset with 365 object categories and 600k images, aimed at advancing object detection research.
-- [**OpenImagesV7**](open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
-- [**SKU-110K**](sku-110k.md): A dataset containing images of densely packed retail products, intended for retail environment object detection.
-- [**VisDrone**](visdrone.md): A dataset focusing on drone-based images, containing various object categories like cars, pedestrians, and cyclists.
-- [**VOC**](voc.md): PASCAL VOC is a popular object detection dataset with 20 object categories including vehicles, animals, and furniture.
-- [**xView**](xview.md): A dataset containing high-resolution satellite imagery, designed for the detection of various object classes in overhead views.
-- [**Brain-tumor**](brain-tumor.md): This dataset comprises MRI or CT scan images containing information about brain tumor presence, location, and characteristics. It plays a crucial role in training computer vision models to automate tumor identification, facilitating early diagnosis and treatment planning.
-- [**African-wildlife**](african-wildlife.md): Featuring images of African wildlife such as buffalo, elephant, rhino, and zebra, this dataset is instrumental in training computer vision models. It is indispensable for identifying animals across different habitats and contributes significantly to wildlife research endeavors.
+- [Argoverse](argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
+- [COCO](coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
+- [LVIS](lvis.md): A large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
+- [COCO8](coco8.md): A smaller subset of the first 4 images from COCO train and COCO val, suitable for quick tests.
+- [Global Wheat 2020](globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
+- [Objects365](objects365.md): A high-quality, large-scale dataset for object detection with 365 object categories and over 600K annotated images.
+- [OpenImagesV7](open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
+- [SKU-110K](sku-110k.md): A dataset featuring dense object detection in retail environments with over 11K images and 1.7 million bounding boxes.
+- [VisDrone](visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
+- [VOC](voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
+- [xView](xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
+- [Roboflow 100](roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
+- [Brain-tumor](brain-tumor.md): A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics.
+- [African-wildlife](african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebras.
+- [Signature](signature.md): A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.
 
 ### Adding your own dataset
 

docs/en/datasets/detect/signature.md

@@ -0,0 +1,90 @@
+---
+comments: true
+description: Signature Detection Dataset, a leading dataset for detecting signatures in documents, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+keywords: Ultralytics, Signature Detection Dataset, object detection, YOLO, YOLO model training, document analysis, computer vision, deep learning models, signature tracking, document verification
+---
+
+# Signature Detection Dataset
+
+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.
+
+## Dataset Structure
+
+The signature detection dataset is split into three subsets:
+
+- **Training set**: Contains 143 images, each with corresponding annotations.
+- **Validation set**: Includes 35 images, each with paired annotations.
+
+## Applications
+
+This dataset can be applied in various computer vision tasks such as object detection, object tracking, and document analysis. Specifically, it can be used to train and evaluate models for identifying signatures in documents, which can have applications in document verification, fraud detection, and archival research. Additionally, it can serve as a valuable resource for educational purposes, enabling students and researchers to study and understand the characteristics and behaviors of signatures in different document types.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file defines the dataset configuration, including paths and classes information. For the signature detection dataset, the `signature.yaml` file is located at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
+
+!!! Example "ultralytics/cfg/datasets/signature.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/signature.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n 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](../../modes/train.md) page.
+
+!!! Example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="signature.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=signature.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+!!! Example "Inference Example"
+
+    === "Python"
+
+        ```python
+        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)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # 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
+        ```
+
+## Sample Images and Annotations
+
+The signature detection dataset comprises a wide variety of images showcasing different document types and annotated signatures. Below are examples of images from the dataset, each accompanied by its corresponding annotations.
+
+![Signature detection dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/88a453da-3110-4835-9ae4-97bfb8b19046)
+
+- **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different signature sizes, aspect ratios, and contexts.
+
+This example illustrates the variety and complexity of images in the signature Detection Dataset, emphasizing the benefits of including mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).

docs/en/datasets/index.md

@@ -35,10 +35,10 @@ Create embeddings for your dataset, search for similar images, run SQL queries,
 Bounding box object detection is a computer vision technique that involves detecting and localizing objects in an image by drawing a bounding box around each object.
 
 - [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
-- [COCO](detect/coco.md): A large-scale dataset designed for object detection, segmentation, and captioning with over 200K labeled images.
+- [COCO](detect/coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
 - [LVIS](detect/lvis.md): A large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
-- [COCO8](detect/coco8.md): Contains the first 4 images from COCO train and COCO val, suitable for quick tests.
-- [Global Wheat 2020](detect/globalwheat2020.md): A dataset of wheat head images collected from around the world for object detection and localization tasks.
+- [COCO8](detect/coco8.md): A smaller subset of the first 4 images from COCO train and COCO val, suitable for quick tests.
+- [Global Wheat 2020](detect/globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
 - [Objects365](detect/objects365.md): A high-quality, large-scale dataset for object detection with 365 object categories and over 600K annotated images.
 - [OpenImagesV7](detect/open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
 - [SKU-110K](detect/sku-110k.md): A dataset featuring dense object detection in retail environments with over 11K images and 1.7 million bounding boxes.
@@ -46,8 +46,9 @@ Bounding box object detection is a computer vision technique that involves detec
 - [VOC](detect/voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
 - [xView](detect/xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
 - [Roboflow 100](detect/roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
-- [Brain-tumor](detect/brain-tumor.md): A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics. It's vital for training computer vision models to automate tumor identification, aiding in early diagnosis and treatment planning.
-- [African-wildlife](detect/african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebra, aids in training computer vision models. Essential for identifying animals in various habitats, it supports wildlife research.
+- [Brain-tumor](detect/brain-tumor.md): A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics.
+- [African-wildlife](detect/african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebras.
+- [Signature](detect/signature.md): A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.
 
 ## [Instance Segmentation Datasets](segment/index.md)
 

mkdocs.yml

@@ -235,6 +235,7 @@ nav:
           - Roboflow 100: datasets/detect/roboflow-100.md
           - Brain-tumor: datasets/detect/brain-tumor.md
           - African-wildlife: datasets/detect/african-wildlife.md
+          - Signature: datasets/detect/signature.md
       - Segmentation:
           - datasets/segment/index.md
           - COCO: datasets/segment/coco.md

ultralytics/cfg/datasets/signature.yaml

@@ -0,0 +1,20 @@
+# 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://ultralytics.com/assets/signature.zip