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license: mit |
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task_categories: |
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- 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** |
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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** |
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- 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. |
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- The annotations are further divided into **train** and **val** subsets to enable training and validation processes. |
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- For TensorFlow users, the dataset includes: |
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- **Train and Val TFRecord files** for easy ingestion by TensorFlow pipelines. |
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- A **label_map.pbtxt** file to map class indices to human-readable class names (e.g., "red," "blue," "yellow"). |
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### **Images** |
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- Images are split into **train** and **val** subsets, matching the corresponding annotations. |
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- Each subset contains subdirectories named after their respective classes (e.g., `red/`, `blue/`, `yellow/`). |
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- Example directory structure: |
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``` |
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dataset/ |
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Images/ |
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train/ |
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red/ |
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img1.jpg |
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img2.jpg |
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blue/ |
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img3.jpg |
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yellow/ |
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img4.jpg |
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val/ |
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red/ |
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img5.jpg |
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blue/ |
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img6.jpg |
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yellow/ |
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img7.jpg |
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``` |
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- This structure ensures compatibility with TensorFlow's `image_dataset_from_directory()` and allows for quick model training. |
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## **Key Features** |
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- **TensorFlow-Optimized Dataset**: Includes TFRecord files and a label map for quick integration. |
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- **Raw XML VOC Annotations**: Flexible format for users preferring other frameworks like PyTorch or custom data pipelines. |
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- **Class-Based Subdirectory Organization**: Simplifies image classification and model evaluation workflows. |
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- **Bounding Boxes**: Precisely annotated around **game pieces of different colors**, ensuring high-quality data for training object detection models. |
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## **Usage** |
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1. **TensorFlow**: |
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- Use the provided TFRecord files (`train.tfrecord` and `val.tfrecord`) for model training. |
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- Load the dataset with TensorFlow's data pipelines and the provided `label_map.pbtxt` file. |
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2. **Custom Frameworks**: |
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- 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**: |
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- The dataset is divided into training (80%) and validation (20%) sets to standardize evaluation. |
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## **Notes** |
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- **Compatibility**: The dataset is designed to be compatible with major machine learning frameworks. |
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- **Expandable**: You can add additional game pieces, classes, or annotations to expand the dataset for future challenges. |
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- **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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