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

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

A Marvelous ChatBot implement using PyTorch.

Official implementation for the paper: "Multi-label Classification with Partial Annotations using Class-aware Selective Loss"

SurfEmb (CVPR 2022) - SurfEmb: Dense and Continuous Correspondence Distributions

Code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving

PyTorch and Tensorflow functional model definitions

We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

PyTorch implementations of the beta divergence loss.

Metadata-Extractor - Metadata Extractor Script can be used to read in exif metadata

AbelNN: Deep Learning Python module from scratch

Autoencoders pretraining using clustering

SimpleDepthEstimation - An unified codebase for NN-based monocular depth estimation methods

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Authors implementation of LieTransformer: Equivariant Self-Attention for Lie Groups

Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"

Implementation of a protein autoregressive language model, but with autoregressive infilling objective (editing subsequences capability)

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

NAS-FCOS: Fast Neural Architecture Search for Object Detection (CVPR 2020)

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon.