SwimAnalysisPro: YOLO11-Pose for Side-View Swimming Analysis
側面游泳姿勢分析模型
Model Description / 模型描述
This model is fine-tuned based on the Ultralytics YOLO11-pose architecture, specifically optimized for detecting and tracking swimmer poses from a side-view perspective. It is designed for technique analysis and biomechanical evaluation in aquatic environments.
本模型基於 Ultralytics YOLO11-pose 架構進行微調，專門針對側面視角的游泳者姿勢進行偵測與追蹤優化。適用於游泳技術分析以及水下或水面的生物力學評估。
Training Dataset / 訓練資料集
The model was trained on a large-scale specialized dataset to ensure robustness across different styles.
本模型使用大規模專用資料集進行訓練，確保在不同泳姿下的穩定性。
- Total Images / 影像總數: 28,000
- Perspective / 視角: Side-view / 側面視角
- Stroke Coverage / 涵蓋泳姿: Butterfly, Backstroke, Breaststroke, and Freestyle / 蝶式、仰式、平式、自由式全涵蓋
Keypoints Index / 關鍵點索引表
The model tracks 7 core human keypoints optimized for swimming stroke analysis:
本模型追蹤 7 個核心人體關鍵點，針對游泳動作分析進行優化：
| Index / 索引 | Keypoint (English) | 關鍵點 (中文) |
|---|---|---|
| 0 | Head | 頭部 |
| 1 | Shoulder | 肩膀 |
| 2 | Elbow | 手肘 |
| 3 | Wrist | 手腕 |
| 4 | Hip | 髖部 |
| 5 | Knee | 膝蓋 |
| 6 | Ankle | 腳踝 |
Performance / 效能指標
The evaluation metrics including mAP50 and mAP50-95 are provided in the charts below.
測試集上的效能數據（如 mAP50 與 mAP50-95）請參考下方圖表。
How to Use / 如何使用
You can load and run this model directly using the Ultralytics Python package:
你可以使用 Ultralytics Python 套件直接載入並執行此模型：
from ultralytics import YOLO
# Load the model
model = YOLO("your_username/your_model_name")
# Run inference
results = model.predict(source="swimming_video.mp4", save=True, conf=0.5)
# Process results
for result in results:
keypoints = result.keypoints.data