torinriley/FTCVision

FTC Vision: 2024-2025 Dataset for Object Detection

This is the official dataset for FTC (FIRST Tech Challenge) object detection for the 2024-2025 season. Designed specifically for training object detection models using TensorFlow, this dataset is also provided in a RAW format for use in other frameworks or custom applications.

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Dataset Overview

The dataset is structured to facilitate seamless integration into TensorFlow pipelines while maintaining flexibility for other use cases. It contains images and annotations in the following structure:

Annotations

• Annotations are provided in XML VOC format, with bounding boxes drawn around each object of interest. Specifically, the dataset focuses on different-colored game pieces for the 2024-2025 season.
• The annotations are further divided into train and val subsets to enable training and validation processes.
• For TensorFlow users, the dataset includes:
  • Train and Val TFRecord files for easy ingestion by TensorFlow pipelines.
  • A label_map.pbtxt file to map class indices to human-readable class names (e.g., "red," "blue," "yellow").

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Images

• Images are split into train and val subsets, matching the corresponding annotations.
• Each subset contains subdirectories named after their respective classes (e.g., red/, blue/, yellow/).
  • Example directory structure:

    dataset/
      Images/
        train/
          red/
            img1.jpg
            img2.jpg
          blue/
            img3.jpg
          yellow/
            img4.jpg
        val/
          red/
            img5.jpg
          blue/
            img6.jpg
          yellow/
            img7.jpg
    

• This structure ensures compatibility with TensorFlow's image_dataset_from_directory() and allows for quick model training.

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Key Features

• TensorFlow-Optimized Dataset: Includes TFRecord files and a label map for quick integration.
• Raw XML VOC Annotations: Flexible format for users preferring other frameworks like PyTorch or custom data pipelines.
• Class-Based Subdirectory Organization: Simplifies image classification and model evaluation workflows.
• Bounding Boxes: Precisely annotated around game pieces of different colors, ensuring high-quality data for training object detection models.

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Usage

1. TensorFlow:

   • Use the provided TFRecord files (train.tfrecord and val.tfrecord) for model training.
   • Load the dataset with TensorFlow's data pipelines and the provided label_map.pbtxt file.

2. Custom Frameworks:

   • Use the raw XML annotations and class-organized image directories for model training in PyTorch or other frameworks.

3. Data Splits:

   • The dataset is divided into training (80%) and validation (20%) sets to standardize evaluation.

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Notes

• Compatibility: The dataset is designed to be compatible with major machine learning frameworks.
• Expandable: You can add additional game pieces, classes, or annotations to expand the dataset for future challenges.
• Standardized Input Size: Images are resized to a consistent shape of (640, 640) for TensorFlow models but can be used at their original resolution in the RAW version.

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This dataset is the ultimate resource for developing and training object detection models tailored to FTC's 2024-2025 game requirements. Let us know if you have any issues or questions!