Update README.md

README.md

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----
-license: mit
----
+---
+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!