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---
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.
---
## **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").
---
### **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.
---
## **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.
---
## **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.
---
## **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.
---
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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