Update README.md

README.md

@@ -17,85 +17,49 @@ datasets:
 - HockeyAI
 ---
 
-# HockeyAI: A Multi-Class Ice Hockey Dataset for Object Detection
+# HockeyAI YOLOv8 Model
 
-The **HockeyAI dataset** is an open-source dataset designed specifically for advancing computer vision research in ice hockey. With approximately **2,100 high-resolution frames** and detailed YOLO-format annotations, this dataset provides a rich foundation for tackling the challenges of object detection in fast-paced sports environments. 
+## Model Overview
 
-The dataset is ideal for researchers, developers, and practitioners seeking to improve object detection and tracking tasks in ice hockey or similar dynamic scenarios.
+The HockeyAI project provides a **YOLOv8 medium model** fine-tuned on the HockeyAI dataset. This model serves as a benchmark for ice hockey object detection tasks and achieves high performance across all seven classes defined in the dataset.
 
----
-
-## Dataset Overview
-
-The HockeyAI dataset includes frames extracted from **broadcasted Swedish Hockey League (SHL) games**. Each frame is manually annotated, ensuring high-quality labels for both dynamic objects (e.g., players, puck) and static rink elements (e.g., goalposts, center ice).
-
-### Classes
-The dataset includes annotations for the following seven classes:
-- **centerIce**: Center circle on the rink
-- **faceoff**: Faceoff dots
-- **goal**: Goal frame
-- **goaltender**: Goalkeeper
-- **player**: Ice hockey players
-- **puck**: The small, fast-moving object central to gameplay
-- **referee**: Game officials
-
-
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/647ceb7936e109abce3e9f1f/g7GiPlsOnaV1pPKhzb_Pz.jpeg)
-
-
-### Key Highlights:
-- **Resolution**: 1920×1080 pixels
-- **Frames**: ~2,100
-- **Source**: Broadcasted SHL videos
-- **Annotations**: YOLO format, reviewed iteratively for accuracy
-- **Challenges Addressed**:
-  - Motion blur caused by fast camera movements
-  - Small object (puck) detection
-  - Crowded scenes with occlusions
-
----
-
-## Applications
-
-The dataset supports a wide range of applications, including but not limited to:
-- **Player and Puck Tracking**: Enabling real-time tracking for tactical analysis.
-- **Event Detection**: Detecting goals, penalties, and faceoffs to automate highlight generation.
-- **Content Personalization**: Dynamically reframing videos to suit different screen sizes.
-- **Sports Analytics**: Improving strategy evaluation and fan engagement.
-
-
----
+## Model Performance
 
-## Pretrained Model
-
-In addition to the dataset, a **YOLOv8 medium model** fine-tuned on the HockeyAI dataset is available. The model achieves high performance across all seven classes and serves as a benchmark for future research. You can access the model by dowloading the pt file under this repo.
-
----
-
-## How to Use the Dataset
-
-1. Download the dataset from [Hugging Face](https://huggingface.co/your-dataset-link).
-2. The dataset is organized in the following structure:
-  HockeyAI/ ├── frames/ │ ├── <Unique_ID>.jpg ├── annotations/ │ ├── <Unique_ID>.txt
+The model was evaluated on a holdout set of the HockeyAI dataset, achieving the following performance metrics:
 
+- **Mean Average Precision (mAP@0.5)**: XX.X%
+- **Precision**: 100% for all classes
+- **Recall**: 95% for all classes
+- **F1-Score**: 93% for all classes
 
-3. Each annotation file follows the YOLO format:
-  <class_id> <x_center> <y_center> <width> <height
+## Usage
 
+The pretrained model is available in this repository as a `.pt` file. You can download and use it directly with the YOLOv8 framework for:
+- Inference on new hockey videos or images
+- Further fine-tuning on your specific use case
+- Benchmarking against new approaches
 
-All coordinates are normalized to the image dimensions.
+## Supported Classes
 
-4. Use the dataset with your favorite object detection framework, such as YOLOv8 or PyTorch-based solutions.
+The model is trained to detect seven classes:
+- Center Ice
+- Faceoff Dots
+- Goal Frame
+- Goaltender
+- Players
+- Puck
+- Referee
 
----
+## Requirements
 
-## Key Metrics
+- YOLOv8 framework
+- Python 3.7+
+- PyTorch 1.7+
 
-The model was evaluated on a holdout set of the HockeyAI dataset, achieving the following performance:
+## Getting Started
 
-- **Mean Average Precision (mAP@0.5)**: XX.X%
-- **Precision**: 100% for all classes
-- **Recall**: 95% for all classes
-- **F1-Score**: 93% for all classes
+1. Download the model weights from this repository
+2. Install the required dependencies
+3. Load and use the model with YOLOv8's standard API