Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Keras version of Realtime Multi-Person Pose Estimation project
| Date | Stars |
|---|---|
| 2026-07-24 | 783 |
| 2026-07-25 | 783 |
| 2026-07-28 | 783 |
| 2026-07-30 | 783 |
| 2026-08-06 | 783 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
#### This repository has become incompatible with the latest and recommended version of Tensorflow 2.0 Instead of refactoring this code painfully, I created a new fresh repository with some additional features like: - #### Training code for smaller model based on MobilenetV2. - #### Visualisation of predictions (heatmaps, pafs) in Tensorboard. - #### Additional scripts to convert and test models for Tensorflow Lite. #### Here is the link to the new repo: [tensorflow_Realtime_Multi-Person_Pose_Estimation](https://github.com/michalfaber/tensorflow_Realtime_Multi-Person_Pose_Estimation) --- # Realtime Multi-Person Pose Estimation (DEPRECATED) This is a keras version of [Realtime Multi-Person Pose Estimation](https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation) project ## Introduction Code repo for reproducing [2017 CVPR](https://arxiv.org/abs/1611.08050) paper using keras. This is a new improved version. The main objective was to remove dependency on separate c++ server which besides the complexity of compiling also contained some bugs... and was very slow. The old version utilizing [rmpe_dataset_server](https://github.com/michalfaber/rmpe_dataset_server) is still available under the tag [v0.1](https://github.com/michalfaber/keras_Realtime_Multi-Person_Pose_Estimation/releases/tag/v0.1) if you really would like to take a look. ## Results <p align="center"> <img src="https://github.com/michalfaber/keras_Realtime_Multi-Person_Pose_Estimation/blob/master/readme/dance.gif", width="720"> </p> <div align="center"> <img src="https://github.com/michalfaber/keras_Realtime_Multi-Person_Pose_Estimation/blob/master/sample_images/ski.jpg", width="300", height="300"> <img src="https://github.com/michalfaber/keras_Realtime_Multi-Person_Pose_Estimation/blob/master/readme/result.png", width="300", height="300"> </div> ## Contents 1. [Converting caffe model](#converting-caffe-model-to-keras-model) 2. [Testing](#testing-steps) 3. [Training](#training-steps) 3. [Changes](#changes) ## Require 1. [Keras](https://keras.io/) 2. [Caffe - docker](https://hub.docker.com/r/bvlc/caffe/) required if you would like to convert caffe model to keras model. You don't have to compile/install caffe on your local machine. ## Converting Caffe model to Keras model Authors of [original implementation](https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation) released already trained caffe model which you can use to extract weights data. - Download caffe model `cd model; sh get_caffe_model.sh` - Dump caffe layers to numpy data `cd ..; docker run -v [absolute path to your keras_Realtime_Multi-Person_Pose_Estimation folder]:/workspace -it bvlc/caffe:cpu python dump_caffe_layers.py` Note that docker accepts only absolute paths so you have to set the full path to the folder containing this project. - Convert caffe model (from numpy data) to keras model `python caffe_to_keras.py` ## Testing steps - Convert caffe model to keras model or download already converted keras model https://www.dropbox.com/s/llpxd14is7gyj0z/model.h5 - Run the notebook `demo.ipynb`. - `python demo_image.py --image sample_images/ski.jpg` to run the picture demo. Result will be stored in the file result.png. You can use any image file as an input. ## Training steps - Install gsutil `curl https://sdk.cloud.google.com | bash`. This is a really helpful tool for downloading large datasets. - Download the data set (~25 GB) `cd dataset; sh get_dataset.sh`, - Download [COCO official toolbox](https://github.com/pdollar/coco) in `dataset/coco/` . - `cd coco/PythonAPI; sudo python setup.py install` to install pycocotools. - Go to the "training" folder `cd ../../../training`. - Optionally, you can set the number of processes used to generate samples in parallel `dataset.py` -> find the line `df = PrefetchDataZMQ(df, nr_proc=4)` - Run the command in terminal `python train_pose.py` ## Changes **25/06/2018** - Performance improvement thanks to replacing c++ se
Excerpt of 6,876 characters
Read on GitHub
27
5
5
Louis Lac · @NeovisionSAS · France
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6f0c9573e7c542fc, topic:computer-vision, topic:pose-estimation, name:pose estimation