Perform video classification using 3D ResNets trained on Kinetics-700 and Moments in Time dataset - accelerated with TensorRT 8.0
P.S Click on the gif to watch the full-length video!
- Activity Recognition TensorRT
- Index
- TensorRT Installation
- Python Dependencies
- Clone the repository
- Download Pretrained Models
- Running the code
- Citations
Assuming you have CUDA already installed, go ahead and download TensorRT 8 from here.
Follow instructions of installing the system binaries and python package for tensorrt here.
Install the necessary python dependencies by running the following command -
pip3 install -r requirements.txtThis is a straightforward step, however, if you are new to git recommend glancing threw the steps.
First, install git
sudo apt install gitNext, clone the repository
# Using HTTPS
https://github.com/kn1ghtf1re/Activity-Recognition-TensorRT.git
# Using SSH
git@github.com:kn1ghtf1re/Activity-Recognition-TensorRT.gitDownload models from google-drive and place them in the current directory.
The code supports a number of command line arguments. Use help to see all supported arguments
➜ python3 action_recognition_tensorrt.py --help
usage: action_recognition_tensorrt.py [-h] [--stream STREAM] [--model MODEL] [--fp16] [--frameskip FRAMESKIP] [--save_output SAVE_OUTPUT]
Action Recognition using TensorRT 8
optional arguments:
-h, --help show this help message and exit
--stream STREAM Path to use video stream
--model MODEL Path to model to use
--fp16 To enable fp16 precision
--frameskip FRAMESKIP
Number of frames to skip
--save_output SAVE_OUTPUT
Save output as videoRun the script this way:
# Video
python3 action_recognition_tensorrt.py --stream /path/to/video --model resnet-18-kinetics-moments.onnx --fp16 --frameskip 2
# Webcam
python3 action_recognition_tensorrt.py --stream webcam --model resnet-18-kinetics-moments.onnx --fp16 --frameskip 2@article{hara3dcnns,
author={Kensho Hara and Hirokatsu Kataoka and Yutaka Satoh},
title={Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?},
journal={arXiv preprint},
volume={arXiv:1711.09577},
year={2017},
}