---
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。

![Results Metrics](results.png)

## 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