RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Last update: Oct 05, 2021

Overview

Emotion recognizer

Clone project
Dataset
Install dependencies
Main program
Demo

1. Clone project

git clone https://github.com/GDSC2021/emotion-recognizer

2. Dataset

Download here

3. Install dependencies

a. Download and install Anaconda:

Download latest version of Python
Download Anaconda package
Installation:
- For macOS
- For Windows
- For Linux

b. Create environment

conda init
conda create -n emotion-detect python=3.8 -y
conda activate emotion-detect

c. Install prerequisites

Open Terminal (macOS and Linux) or Anaconda Prompt (Windows)
Go to your cloned directory

Run:

For Windows

pip install matplotlib pandas pillow autopep8 sklearn tensorflow keras opencv-python seaborn

For macOS

chmod +x dependencies/mac/dependencies.sh && ./dependencies/mac/dependencies.sh

For Linux

chmod +x dependencies/linux/linux.sh && ./dependencies/linux/linux.sh

4. Run main program

Run program:

python src/main.py  #For Windows
python3 src/main.py #For macOS and Linux

To abort program, press the combination of Ctrl + C

5. Demo

RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Related tags

Overview

Emotion recognizer

Table of contents

1. Clone project

2. Dataset

3. Install dependencies

a. Download and install Anaconda:

b. Create environment

c. Install prerequisites

4. Run main program

5. Demo

Owner

Google Developer Student Club - UIT

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RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Related tags

Overview

Emotion recognizer

Table of contents

1. Clone project

2. Dataset

3. Install dependencies

a. Download and install Anaconda:

b. Create environment

c. Install prerequisites

4. Run main program

5. Demo

Owner

Google Developer Student Club - UIT

Unofficial PyTorch Implementation of AHDRNet (CVPR 2019)

NovelD: A Simple yet Effective Exploration Criterion

Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)

Benchmark VAE - Library for Variational Autoencoder benchmarking

"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

Deep Inside Convolutional Networks - This is a caffe implementation to visualize the learnt model

GrabGpu_py: a scripts for grab gpu when gpu is free

Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

Deep learning toolbox based on PyTorch for hyperspectral data classification.

CellRank's reproducibility repository.

Code and datasets for the paper "Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction" (RA-L, 2021)

Tiny Object Detection in Aerial Images.

for taichi voxel-challange event

Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"

A Simple Key-Value Data-store written in Python

Generate high quality pictures. GAN. Generative Adversarial Networks

Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

2021搜狐校园文本匹配算法大赛 分比我们低的都是帅哥队

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