sain0722/EfficientNet-Visualizer

PyQT 기반 GUI, EfficientNet의 학습 결과를 Grad-CAM을 이용하여 확인할 수 있습니다.

EfficientNet-Visualizer

EfficientNet 모델 결과를 Inference하는 프로그램입니다. Grad-CAM을 사용하여 시각화한 결과를 확인할 수 있습니다.

Installation

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Requirements

All the codes are tested in the following environment:

• Windows 10
• GPU: RTX 30xx
• Python 3.7
• torch 1.12.1
• torchvision 0.13.1

Check Your CUDA Version

GTX 1650 ~ RTX 2080

CUDA 10~10.2

CUDNN 7.5 (Turing)

RTX 3050~3090

CUDA 11.1 ~ 11.4

CUDNN 8.6 (Ampere)

RTX 4060 ~ 4090

CUDA 11.8 / 12.0~12.3

CUDNN 8.9 (Ada Lovelace)

1. Install PyTorch

Install PyTorch for your GPU.

• Only CPU (Conda)

conda install pytorch==1.12.1 torchvision==0.13.1 -c pytorch

• Only CPU (pip)

pip install torch==1.12.1 torchvision==0.13.1

• GPU

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

2. Install EfficientNet-PyTorch

Install with pip install efficientnet_pytorch and load a pretrained EfficientNet with:

from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')

3. Install Grad-CAM for PyTorch

pip install grad-cam

from pytorch_grad_cam import GradCAM

4. Install requirements

pip install -r requirements.txt

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Quick Demo

python main.py