Swimmer

--- license: mit base_model: - AXERA-TECH/YOLO11-Pose pipeline_tag: keypoint-detection tags: - sports - swimming - pose-estimation --- # 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： ```python 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

Source context: 0 downloads · 1 likes · Pipeline keypoint-detection · Repo KadyT/Swimmer