RepNet PyTorch

GitHub repository: https://github.com/materight/RepNet-pytorch.

A PyTorch port with pre-trained weights of RepNet, from Counting Out Time: Class Agnostic Video Repetition Counting in the Wild (CVPR 2020) [paper] [project] [notebook].

This repo provides an implementation of RepNet written in PyTorch and a script to convert the pre-trained TensorFlow weights provided by the authors. The outputs of the two implementations are almost identical, with a small deviation (less than $10^{-6}$ at most) probably caused by the limited precision of floating point operations.

Get Started

Clone this repo and install dependencies:

git clone https://github.com/materight/RepNet-pytorch
cd RepNet-pytorch
pip install -r requirements.txt

To download the TensorFlow pre-trained weights and convert them to PyTorch, run:

python convert_weights.py

Run inference

Simply run:

python run.py

The script will download a sample video, run inference on it and save the count visualization. You can also specify a video path as argument (either a local path or a YouTube/HTTP URL):

python run.py --video_path [video_path]

If the model does not produce good results, try to run the script with more stride values using --strides.

Example of generated videos showing the repetition count, with the periodicity score and the temporal self-similarity matrix: <div align="center"> <img src="https://raw.githubusercontent.com/materight/RepNet-pytorch/main/img/example5_score.gif" height="200" /> <img src="https://raw.githubusercontent.com/materight/RepNet-pytorch/main/img/example5_tsm.png" height="200" /> </div>

RepNet PyTorch

Get Started

Run inference

NSDT 3DConvert

UnrealSynth

DreamTexture.js