Deep TEN: Deep Texture Encoding Network Example¶

In this section, we show an example of training/testing Encoding-Net for texture recognition on MINC-2500 dataset. Comparing to original Torch implementation, we use different learning rate for pre-trained base network and encoding layer (10x), disable color jittering after reducing lr and adopt much smaller training image size (224 instead of 352).

Test Pre-trained Model¶

Clone the GitHub repo:

git clone https://github.com/zhanghang1989/PyTorch-Encoding

Install PyTorch Encoding (if not yet). Please follow the installation guide Installing PyTorch Encoding.

Download the MINC-2500 dataset using the providied script:

cd PyTorch-Encoding/
python scripts/prepare_minc.py

Test pre-trained model on MINC-2500. The pre-trained weight will be automatic downloaded (pre-trained on train-1 split using single training size of 224, with an error rate of \(18.96\%\) using single crop on test-1 set):

python verify.py --dataset minc --model deepten_resnet50_minc
# Teriminal Output:
# Top1: 81.043 | Top5: 95.617: 100%|███████████████████████████████████| 45/45 [00:18<00:00,  2.40it/s]
# Top1 Acc: 81.043 | Top5 Acc: 95.617

Train Your Own Model¶

Example training command for training above model:

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_dist.py --dataset minc --model deepten_resnet50_minc --batch-size 512 --lr 0.004 --epochs 80 --lr-step 60 --lr-scheduler step --weight-decay 5e-4

Detail training options:

-h, --help            show this help message and exit
--dataset DATASET     training dataset (default: cifar10)
--model MODEL         network model type (default: densenet)
--backbone BACKBONE   backbone name (default: resnet50)
--batch-size N        batch size for training (default: 128)
--test-batch-size N   batch size for testing (default: 1000)
--epochs N            number of epochs to train (default: 300)
--start_epoch N       the epoch number to start (default: 0)
--lr LR               learning rate (default: 0.1)
--momentum M          SGD momentum (default: 0.9)
--weight-decay M      SGD weight decay (default: 1e-4)
--no-cuda             disables CUDA training
--plot                matplotlib
--seed S              random seed (default: 1)
--resume RESUME       put the path to resuming file if needed
--checkname           set the checkpoint name
--eval                evaluating

Citation¶

Note

Hang Zhang, Jia Xue, and Kristin Dana. “Deep TEN: Texture Encoding Network.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017:

@InProceedings{Zhang_2017_CVPR,
author = {Zhang, Hang and Xue, Jia and Dana, Kristin},
title = {Deep TEN: Texture Encoding Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}