Deep TEN: Deep Texture Encoding Network Example

../_images/cvpr17.svg

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
    
  • Download pre-trained model (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):

    cd experiments/recognition
    python model/download_models.py
    
  • Test pre-trained model on MINC-2500:

    python main.py --dataset minc --model deepten --nclass 23 --resume deepten_minc.pth --eval
    # Teriminal Output:
    # Loss: 1.005 | Err: 18.96% (1090/5750): 100%|████████████████████| 23/23 [00:18<00:00,  1.26it/s]
    

Train Your Own Model

  • Example training command for training above model:

    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --dataset minc --model deepten --nclass 23 --model deepten --batch-size 512 --lr 0.004 --epochs 80 --lr-step 60 --lr-scheduler step
    
  • 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
    

Extending the Software

This code is well written, easy to use and extendable for your own models or datasets:

  • Write your own Dataloader mydataset.py to dataset/ folder

  • Write your own Model mymodel.py to model/ folder

  • Run the program:

    python main.py --dataset mydataset --model mymodel
    

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}
    }