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| license: mit |
| task_categories: |
| - object-detection |
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| # FTC Vision: 2024-2025 Dataset for Object Detection |
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| 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: |
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| ### **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. |
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| 2. **Custom Frameworks**: |
| - Use the raw XML annotations and class-organized image directories for model training in PyTorch or other frameworks. |
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| 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! |
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