rm cifar exp (#293)

Co-authored-by: hzaws <hzaws@u8bc4deae3e4a50.ant.amazon.com>

docs/source/tutorials/cifar.rst

-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 <../notes/compile.html>`_.
-
-- Download pre-trained EncNet-32k128d model::
-
-    cd PyTorch-Encoding/experiments/recognition
-    bash model/download_models.sh
-
-.. _curve:
-
-.. image:: ../_static/img/EncNet32k128d.svg
-    :width: 70%
-
-- Test EncNet-32k128d pre-trained model (training `curve`_ of this model is shown above, with a final error rate of :math:`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
-
-
-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}
-        }