deep-high-resolution-net.pytorch
mmpose
| deep-high-resolution-net.pytorch | mmpose | |
|---|---|---|
| 4 | 31 | |
| 4,190 | 5,025 | |
| - | 2.8% | |
| 0.0 | 8.0 | |
| over 1 year ago | 12 days ago | |
| Cuda | Python | |
| MIT License | Apache License 2.0 |
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deep-high-resolution-net.pytorch
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Deadlift videos needed [AI]
I am sure you have some great ideas in mind for your model design, but a quick tip (from having worked on a similar project) - it may be very challenging to simply take raw videos as input (especially with unconstrained camera viewpoints) without doing some feature extraction/keypoint detection first. For example, open-source plug-and-play 2D keypoint detectors are extremely good nowadays (e.g HRNet), and explicitly detecting 2D keypoints before classifying deadlift videos as "good" or "bad" will likely make the task more feasible!
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[D] HRNET- Human Pose Estimation [Implementation]
The repo is well maintained on GitHub and I have replicated the author's work. But I didn't get much understanding of how things are working there. I didn't see any results.
- Help finding an appropriate model for human pose estimation
- Real time 3d reconstruction with known parameters of cameras and environment?
mmpose
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RTMPose: The All-In-One Real-time Pose Estimation Solution for R&D
RTMPose-m achieves 75.8% AP on COCO with 90+ FPS on an Intel i7-11700 CPU and 430+ FPS on an NVIDIA GTX 1660 Ti GPU, and RTMPose-l achieves 67.0% AP on COCO-WholeBody with 130+ FPS.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMPose: OpenMMLab pose estimation toolbox and benchmark.
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Model conversion from Pytorch to Tf using Onnx.
I downloaded pytorch2onnx.py from mmPose tools. It's big, but the top half is imports and input arguments. Line 125, I hard-coded my (image) input size. I ran it on my .pth model file, and out pop'd an onnx file.
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Finetuning Openpose for custom dataset
They have a specific repo called mmpose: https://github.com/open-mmlab/mmpose
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State of the art 2D body pose estimation [Discussion]
I would start with mmpose. It's basically a curated list of the best models ready to go.
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[P] Object detection framework : Detectron2 VS MMDetection
The [MMLab key point detection](https://github.com/open-mmlab/mmpose) is in a separate repo from detection.
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[D] Searching for open source pose estimation solution similar to open pose ?
One option is mmPose. They have a bunch of 2D/3D models implemented and support different skeleton structures.
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Human Pose Estimation Recommendation
This library is pretty good. It has implementations for a number of pose estimators. I think Darkpose is the best one from memory
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Human pose classification problem.
Check out https://github.com/open-mmlab/mmpose I think they have guides for new datasets
What are some alternatives?
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
lightweight-human-pose-estimation.pytorch - Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
AdelaiDet - AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
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
mmfewshot - OpenMMLab FewShot Learning Toolbox and Benchmark