DREAM
lightweight-human-pose-estimation.pytorch
| DREAM | lightweight-human-pose-estimation.pytorch | |
|---|---|---|
| 1 | 2 | |
| 139 | 2,021 | |
| 0.7% | - | |
| 3.5 | 0.0 | |
| 7 months ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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DREAM
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Pose estimation
Here's a deep-learning approach that has pretty great results: https://github.com/NVlabs/DREAM
lightweight-human-pose-estimation.pytorch
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Help finding an appropriate model for human pose estimation
Lightweight OpenPose: Runs in realtime >20fps confirmed, training code is provided
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How do I properly dissect a Github repo of a ML model?
Using https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch as an example (or another repo, that was just one I found), could someone please give me a step by step process of how they read a repo for a research paper?
What are some alternatives?
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
kapao - KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
posenet-python - A Python port of Google TensorFlow.js PoseNet (Real-time Human Pose Estimation)
trt_pose - Real-time pose estimation accelerated with NVIDIA TensorRT
tfjs - A WebGL accelerated JavaScript library for training and deploying ML models.
UniPose - We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datase
tfjs-models - Pretrained models for TensorFlow.js