Histology images query (unsupervised)

Last update: Jan 05, 2022

Overview

110-1-NTU-DBME5028-Histology-images-query

Final Project: Histology images query (unsupervised) Kaggle: https://www.kaggle.com/c/histology-images-query-competition/overview

Teem members:
f09921058 陳羿翔, r09942171 黃繼綸

Environment

OS : Ubuntu 16.04
Language: Python37 Torch: 21.08+

Introduction

Processing

Model (VAE + BYOL)

How to use

Download dataset in Kaggle

https://www.kaggle.com/c/histology-images-query-competition/data

$ kaggle competitions download -c histology-images-query-competition

Download the pretrained models

https://drive.google.com/drive/folders/1u2wWuPfb327AHGi8WRCTMiVxmIRPrUHg?usp=sharing

Training from scratch

$ python train.py -v --eta 10 --save_model_path path/to/save --data path/to/train

-v: save vae result
--eta: Ltotal = Ltask + \eta * Lreconstruction
--lamda: Lconstruction = Lmse + \lamda * Lkl

Testing

$ python inference.py --data path/to/test

Owner

GitHub Repository

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Histology images query (unsupervised)

Related tags

Overview

110-1-NTU-DBME5028-Histology-images-query

Environment

Introduction

Processing

Model (VAE + BYOL)

How to use

Download dataset in Kaggle

Download the pretrained models

Training from scratch

Testing

Owner

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