Tree-Classification

We customize pointnet.pytorch for the binary tree classification.

We add tree dataloader for the classification and pointnet_demo.ipynb for colab users.

Table of contents

• Environment Setting

• Usage

• Demo

Environment Setting

If you want to train this model, you should install below library. If you don't want to install libraries, you can use our demo file too.

• python 3.9.12
• torch 1.0.2
• sklearn 1.10.1+cu102

pip install torch
pip install sklearn

Usage

You can use .off or .ply but My code will convert your .off files to .ply files. If you don't want to change it, give convert_off_to_ply parameter to False

Dataset

If you want to make your own dataset, change paths of dataset (larch, pine) and run train_valid_maker.py

Also, add id.txt to misc directory for your tast. Here is example

python train_valid_maker.py

Train model

cd ./utils
python train_classification.py --batchSize [batch size] --num_points [number of points for each data] --nepoch [training epoch] --dataset [dataset path] --dataset_type [dataset type]

example

cd ./utils
python train_classification.py --dataset ./dataset --dataset_type tree

Test model

cd ./utils
python train_classification.py --batchSize [batch size] --num_points [number of points for each data] --nepoch [training epoch] --dataset [dataset path] --dataset_type [dataset type] -train False

example

cd ./utils
python train_classification.py --dataset ./dataset --dataset_type tree -train False

Demo

If you want to run demo file, you can use pointnet_demo.ipynb.

Project Period

Oct 24, 2022 - Nov 30, 2022