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pytorch-classification's Introduction

Pytorch Classification

This project was created to train a single classification model.

Training

python helper/train.py +configs=<CONFIG>

Installation

$git clone https://github.com/pedrodiamel/pytorchvision.git
$cd pytorchvision
$python setup.py install

Training visualize

We now support Visdom for real-time loss visualization during training!

To use Visdom in the browser:

# First install Python server and client
pip install visdom
# Start the server (probably in a screen or tmux)
python -m visdom.server -env_path out/runs/visdom/ -port 6006
# http://localhost:6006/

For jupyter notebook

jupyter notebook --port 8080 --allow-root --ip 0.0.0.0 --no-browser

Docker

docker build -f "Dockerfile" -t torchcls:latest .
docker run -ti --privileged --ipc=host --name torchcls-dev -p 8080:8080 -p 6006:6006 -v $DATASETS:/.datasets torchcls:latest /bin/bash

Dockercompose

docker-compose up --build -d
docker-compose down
docker exec -it torchcls-dev /bin/bash

Benchmarking

Accuracy

Model CIFAR10 CIFAR100 FERp Affect
PreActResNet18 95.36% 77.02% 87.25 43.0
PreActResNet34 95.72% 78.83%

Accuracy FER problem

Model Ferp(test) AffectNet(val) Ckp Jaffe BU3DFE Models
PreActResNet18 82.372 26,100 55,307 36,318 39,828
FMPNet 79,535 29,200 65,363 46,766 41,379
CVGG 84,316 31,150 66,201 46,269 42,069
ResNet18 87,695 34,400 71,508 50,746 45,345
AlexNet 86,038 35,075 70,670 64,401 46,379
DeXpression 79,694 31,875 51,117 44,279 37,241

References

pytorch-classification's People

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