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.pyAccuracy & Statistics
| Base Model | Dataset | Method | Accuracy | Pretrained Model |
|---|---|---|---|---|
| MobileNet | GTOS | Finetune | 80.4 | One Drive |
| DAIN | 82.5 | |||
| DEP | 83.3 | |||
| TEAN | 84.7 | |||
| ResNet-50 | DTD | DEP | 73.2 | |
| MINC | DEP | 82.0 |
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}
}
Differential Viewpoints for Ground Terrain Material Recognition [pdf][arxiv]
@article{xue2020differential,
title={Differential Viewpoints for Ground Terrain Material Recognition},
author={Xue, Jia and Zhang, Hang and Nishino, Ko and Dana, Kristin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2020},
publisher={IEEE}
}
Acknowledgement
Part of the code comes from PyTorch-Encoding, TorchSeg, pytorch-mobilenet-v2