---
license: mit
task_categories:
- object-detection
---

# 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!