3D ResNet Video Classification accelerated by TensorRT

Related tags

Deep Learning python cuda video-classification tensorrt
Overview

Activity Recognition TensorRT

Perform video classification using 3D ResNets trained on Kinetics-400 dataset and accelerated with TensorRT

ActivityGIF

P.S Click on the gif to watch the full-length video!

Index

TensorRT Installation

Assuming you have CUDA already installed, go ahead and download TensorRT from here.

Follow instructions of installing the system binaries and python package for tensorrt here.

Python dependencies

Install the necessary python dependencies by running the following command -

pip3 install -r requirements.txt

Clone the repository

This is a straightforward step, however, if you are new to git recommend glancing threw the steps.

First, install git

sudo apt install git

Next, clone the repository

# Using HTTPS
https://github.com/aj-ames/Activity-Recognition-TensorRT.git
# Using SSH
[email protected]:aj-ames/Activity-Recognition-TensorRT.git

Download Pretrained Models

Download models from google-drive and place them in the current directory.

Running the code

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]

Object Detection using YOLOv4 and OpenCV4

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

Run the script this way:

# Video
python3 action_recognition_tensorrt.py --stream /path/to/video --model resnext-101-kinetics.onnx --fp16 --frameskip 3

# Webcam
python3 action_recognition_tensorrt.py --stream webcam --model resnext-101-kinetics.onnx --fp16 --frameskip 3

Citations

@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},
}
You might also like...
WHENet - ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L
WHENet - ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L

HeadPoseEstimation-WHENet-yolov4-onnx-openvino ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L 1. Usage $ git clone htt

Katsuya Hyodo 49 Sep 21, 2022
Convert openmmlab (not only mmdetection) series model to tensorrt

MMDet to TensorRT This project aims to convert the mmdetection model to TensorRT model end2end. Focus on object detection for now. Mask support is exp

JinTian 4 Dec 17, 2021
Using image super resolution models with vapoursynth and speeding them up with TensorRT

vs-RealEsrganAnime-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Also a docker image since

null 4 Aug 23, 2022
Using VapourSynth with super resolution models and speeding them up with TensorRT.

VSGAN-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Using NVIDIA/Torch-TensorRT combined wi

null 111 Jan 5, 2023
This project aims to explore the deployment of Swin-Transformer based on TensorRT, including the test results of FP16 and INT8.

Swin Transformer This project aims to explore the deployment of SwinTransformer based on TensorRT, including the test results of FP16 and INT8. Introd

maggiez 87 Dec 21, 2022
The modify PyTorch version of Siam-trackers which are speed-up by TensorRT.

SiamTracker-with-TensorRT The modify PyTorch version of Siam-trackers which are speed-up by TensorRT or ONNX. [Updating...] Examples demonstrating how

null 9 Dec 13, 2022
Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.
Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Matthias Wright 169 Dec 26, 2022
improvement of CLIP features over the traditional resnet features on the visual question answering, image captioning, navigation and visual entailment tasks.

CLIP-ViL In our paper "How Much Can CLIP Benefit Vision-and-Language Tasks?", we show the improvement of CLIP features over the traditional resnet fea

null 310 Dec 28, 2022
PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"

R2Plus1D-PyTorch PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal

Irhum Shafkat 342 Dec 16, 2022
Comments
  • 3D ResNet-PyTorch to ONNX conversion

    3D ResNet-PyTorch to ONNX conversion

    Hi @kn1ghtf1re I am amazed to see the results. Will you please share the conversion part also. This repo has only the inference of TensorRT model. I am very interested and excited to know the conversion. Thanks

    opened by neso613 2
Owner
Akash James
Tech Savage.
Akash James
GitHub
Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)

Image Classification Project Killer in PyTorch This repo is designed for those who want to start their experiments two days before the deadline and ki

null 349 Dec 8, 2022
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.

cdf_att_classification classes = {0: 'cat', 1: 'dog', 2: 'flower'} In this project we use both Resnet and Self-attention layer for cdf-Classification.

null 3 Nov 23, 2022
tensorrt int8 量化yolov5 4.0 onnx模型

onnx模型转换为 int8 tensorrt引擎

null 123 Dec 28, 2022
PyTorch ,ONNX and TensorRT implementation of YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

null 4.2k Jan 1, 2023
EfficientNetv2 TensorRT int8

EfficientNetv2_TensorRT_int8 EfficientNetv2模型实现来自https://github.com/d-li14/efficientnetv2.pytorch 环境配置 ubuntu：18.04 cuda：11.0 cudnn：8.0 tensorrt：7

null 34 Apr 24, 2022
TensorRT examples (Jetson, Python/C++)(object detection)

TensorRT examples (Jetson, Python/C++)(object detection)

Nobuo Tsukamoto 53 Dec 22, 2022
Export CenterPoint PonintPillars ONNX Model For TensorRT

CenterPoint-PonintPillars Pytroch model convert to ONNX and TensorRT Welcome to CenterPoint! This project is fork from tianweiy/CenterPoint. I impleme

CarkusL 149 Dec 13, 2022
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our report on Arxiv.

null 7.7k Jan 6, 2023
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

null 7.7k Jan 3, 2023
This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

PyTorch Infer Utils This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model infer

Alex Gorodnitskiy 11 Mar 20, 2022