Efficient YoloPose is a deep learning-based architecture designed to balance computational efficiency and accuracy for real-time multi-person pose estimation. This repository contains the implementation of Efficient YoloPose, which improves upon YOLOv8-Pose by incorporating advanced lightweight techniques, reducing computational demands while maintaining high performance, particularly for resource-constrained devices.

Human pose estimation (HPE) is crucial for various real-time applications, but achieving high accuracy with low computational overhead is a challenge, especially on resource-constrained devices. Efficient YoloPose addresses this challenge by optimizing YOLOv8-Pose with lightweight components to achieve both efficiency and accuracy. Efficient design: Utilizes Depthwise Convolution, Ghost Convolution, and C3Ghost to reduce model size and complexity. Advanced attention mechanism: Integrates Squeeze Excitation (SE) attention for improved feature extraction. Superior performance: Reduces inference time, computational complexity, and parameter count compared to traditional models like YOLOv8-Pose.

- Improved Inference Time: The proposed model reduces the inference time from 1.1 ms to 0.9 ms.

- Reduced Computational Complexity: The computational complexity decreases from 9.2 GFlops to 4.8 GFlops.

- Lower Parameter Count: The parameter count is reduced from 3.3 million to 1.3 million.

- Accuracy: The model achieves an average precision (AP) score of 78.8 on the COCO dataset, outperforming other models in both efficiency and accuracy.

- Lightweight Design: Optimized for resource-constrained devices while maintaining high performance.

When compared to YOLOv8-Pose, Efficient YoloPose: .

- Reduces inference time

- Lowers computational complexity

- Reduces parameter count

- Maintains competitive accuracy

The model was evaluated on the following datasets: .

- COCO: Common Objects in Context, a large-scale object detection dataset.

- OCHuman: A dataset specifically designed for human pose estimation.

git clone https://github.com/malareeqi/Efficient-YoloPose.git
cd Efficient-YoloPose

pip install -r requirements.txt

%cd /Efficient-YoloPose/ultralytics/models/yolo/pose/
python train.py

@article{EfficientYoloPose,
    title={Resource-Aware Strategies for Real-Time Multi-Person Pose Estimation},
    author={Mohammed A. Esmail, Jinlei Wang,*,Yihao Wang, Li Sun, Guoliang Zhu, and Guohe Zhang*}
    journal={XXXX.XXXX},
    year={2024}
}