EncNet on CIFAR-10

Test Pre-trained Model

  • Clone the GitHub repo:

    git clone git@github.com:zhanghang1989/PyTorch-Encoding.git
    
  • Install PyTorch Encoding (if not yet). Please follow the installation guide Installing PyTorch Encoding.

  • Download pre-trained EncNet-32k128d model:

    cd PyTorch-Encoding/experiments/recognition
    bash model/download_models.sh
    
../_images/EncNet32k128d.svg
  • Test EncNet-32k128d pre-trained model (training curve of this model is shown above, with a final error rate of \(3.35\%\)):

    >>> python main.py --dataset cifar10 --model encnetdrop --widen 8 --ncodes 32 --resume model/encnet_cifar.pth.tar --eval
    # Teriminal Output:
    #Loss: 0.129 | Err: 3.350% (335/10000): 100%|█████████████████████████████████████████████| 79/79 [00:49<00:00,  1.58it/s]
    # Error rate is 3.350
    

Train Your Own Model

  • Example training command for training above model:

    CUDA_VISIBLE_DEVICES=0,1 python main.py --dataset cifar10 --model encnetdrop --widen 8 --ncodes 32 --lr-scheduler cos --epochs 600 --checkname mycheckpoint
    
  • Detail training options:

    -h, --help            show this help message and exit
    --dataset DATASET     training dataset (default: cifar10)
    --model MODEL         network model type (default: densenet)
    --widen N             widen factor of the network (default: 4)
    --ncodes N            number of codewords in Encoding Layer (default: 32)
    --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, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal. “Context Encoding for Semantic Segmentation” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018:

    @InProceedings{Zhang_2018_CVPR,
    author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
    title = {Context Encoding for Semantic Segmentation},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2018}
    }