Skip to content
Material Classification with Convolutional Networks in PyTorch
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
dataloader
dataset
experiments
model
utils
.gitignore
LICENSE
README.md

README.md

Pytorch Material Classification

This repo provides examples for material classification in GTOS, GTOS-MOBILE, DTD and MINC dataset using PyTorch.

Setup

Prerequisites

  • Ubuntu
  • Pytorch
    • pip3 install torch torchvision
  • Easydict
    • pip3 install easydict
  • tqdm
    • pip3 install tqdm
  • Pytorch-Encoding
    • pip3 install torch-encoding
    • Note: You need to install Pytorch 1.0 for torch-encoding, or you can modify the encoding layer based on this for latest Pytorch.

Getting Started

  • Clone this repo:
git clone https://github.com/jiaxue1993/pytorch-material-classification.git
cd pytorch-material-classification/
  • Download GTOS, GTOS_MOBILE, DTD, MINC to the dataset folder

  • Navigate to different experiment folder and train network. For example, you can finetune ResNet on GTOS-MOBILE dataset with followint command

cd experiments/gtos_mobile.finetune.resnet/
python train.py

Accuracy & Statistics

Coming soon

Citation

Please consider citing following projects in your publications if it helps your research.

Differential Angular Imaging for Material Recognition [pdf]

@inproceedings{xue2017differential,
  title={Differential angular imaging for material recognition},
  author={Xue, Jia and Zhang, Hang and Dana, Kristin and Nishino, Ko},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={764--773},
  year={2017}
}

Deep Texture Manifold for Ground Terrain Recognition [pdf]

@inproceedings{xue2018deep,
  title={Deep texture manifold for ground terrain recognition},
  author={Xue, Jia and Zhang, Hang and Dana, Kristin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={558--567},
  year={2018}
}

Deep TEN: Texture Encoding Network [pdf]

@inproceedings{zhang2017deep,
  title={Deep ten: Texture encoding network},
  author={Zhang, Hang and Xue, Jia and Dana, Kristin},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={708--717},
  year={2017}
}

Acknowledgement

Part of the code comes from PyTorch-Encoding, TorchSeg, pytorch-mobilenet-v2

You can’t perform that action at this time.