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Overview

The Visual Layer bounding box annotation format is stored in CSV format, making it easy to process and integrate with various machine learning workflows. Each row represents a single bounding box annotation for an object within an image.

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CSV Format Structure

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Example Annotation

filename,col_x,row_y,width,height,label
image1.jpg,50,30,200,150,car
image2.jpg,120,60,80,100,person
image3.jpg,15,10,50,70,dog

The top 5 common annotation formats for bounding boxes in image datasets include:

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1.COCO (Common Objects in Context) Format

• Bounding Box Format: [x_min, y_min, width, height]
• Details: Uses JSON format with a dictionary structure. The bounding box is represented by the top-left (x_min, y_min) coordinate, along with width and height.
• Example:

  {
    "bbox": [100, 150, 200, 300]
  }
  

• Used In: Microsoft COCO dataset.

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2.PASCAL VOC (Visual Object Classes) Format

• Bounding Box Format: (x_min, y_min, x_max, y_max)
• Details: Stored in XML files using a <bndbox> tag. The bounding box is represented using absolute pixel values for top-left (x_min, y_min) and bottom-right (x_max, y_max) coordinates.
• Example:

  <bndbox>
    <xmin>100</xmin>
    <ymin>150</ymin>
    <xmax>300</xmax>
    <ymax>450</ymax>
  </bndbox>
  

• Used In: VOC 2012 dataset.

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3.YOLO (You Only Look Once) Format

• Bounding Box Format: [x_center, y_center, width, height], normalized (0 to 1 scale)
• Details: Uses plain text .txt files where each row represents an object as class_id x_center y_center width height, with values normalized relative to the image width and height.
• Example:

  0 0.5 0.6 0.25 0.4
  

• Used In: YOLO models and datasets.

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4.TFRecord / TensorFlow Object Detection API Format

• Bounding Box Format: Normalized (y_min, x_min, y_max, x_max) (0 to 1 scale)
• Details: Uses TFRecord format to store images and annotations in protocol buffers (Protobuf). Bounding boxes are stored as normalized float values within .tfrecord files.
• Example(within a .record file, usually not human-readable):

  {
    "ymin": 0.25,
    "xmin": 0.20,
    "ymax": 0.75,
    "xmax": 0.80
  }
  

• Used In: TensorFlow Object Detection API.

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5.LabelMe Format

• Bounding Box Format: (x_min, y_min, x_max, y_max)
• Details: Uses JSON files similar to COCO but with a different structure, including polygon annotations. Bounding boxes are stored as [x_min, y_min, x_max, y_max].
• Example:

  {
    "shapes": [
      {
        "label": "dog",
        "points": [[100, 150], [300, 450]],
        "shape_type": "rectangle"
      }
    ]
  }
  

• Used In: LabelMe annotation tool.

###Summary of Bounding Box Formats

Each format is optimized for different use cases, with COCO and VOC being the most widely used in academic datasets, YOLO for real-time detection, TFRecord for TensorFlow-based training, and LabelMe for manual annotations.

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Converting to Visual Layer format

1. Convert from rom VOC2012 to Visual Layer Object Detection

📘 VL Bounding Boxes

2. Convert from rom Coco to Visual Layer Object Detection

📘 VL Bounding Boxes