Training images are blurred by Gaussian function. The images are more blurred as std.(σ) of Gaussian kernel is bigger.

You can try different training schedule as for blurring images. Here is an overview of the training schedule:

• Install Python Packages

$ pip install -r requirements.txt

Or pull and run docker image (e.g. blur-training:latest) I made for this training.

• Get ImageNet images & set the path. If you already have ImageNet, set IMAGENET_PATH variable in training/main.py.
  If not, Download the ImageNet dataset from http://www.image-net.org/
  (Note that the ImageNet images need to be divided in two subdirectories, train/ and val/.)
  Then set the path.

General usage example:

$ cd training
$ python main.py --arch [ARCHITECTURE NAME] --mode [TRAINING MODE] -n [EXPERIMENT NAME] 

For main.py, you need to use --exp_name or -n option to define your experiment's name. Then the experiment's name is used for managing results under logs/ directory. logs/ will automatically be created when you run main.py.
You can choose the training mode from:
normal, all, mix, reversed-single-step, single-step, multi-steps
by using --mode [TRAINING MODE] option.

• normal
  This mode trains Normal model (default: AlexNet).
  usage example:

$ python main.py --arch alexnet --mode normal -e 60 -b 64 --lr 0.01 -n alexnet_normal

You can also specify GPU by --gpu.
(You don't need it if you have only single GPU in your machine.)

$ python -u main.py --gpu 0 -a alexnet --mode normal -n alexnet_normal

• all
  This mode blurs ALL images in the training mode.
  usage example:

$ python main.py --arch alexnet --mode all -s1 -n alexnet_all_s1

• mix
  This mode blurs half training data. usage example:

$ python main.py --arch alexnet --mode mix -s 1 -n alexnet_mix_s1

• random-mix
  This mode blurs half training data randomly.
  usage example:

$ python main.py --arch alexnet --mode random-mix --min_sigma 0 --max_sigma 5 -n alexnet_random-mix_s0-5

• single-step
  This mode blurs first half epochs (e.g. first 30 epochs in 60 entire epochs) in the training. usage example:

$ python main.py --arch alexnet --mode single-step -s 1 -n alexnet_single-step_s1

• fixed-single-step
  This mode blurs first half epochs in the training, then fixes the weights of 1st Conv layer. usage example:

python -u main.py -a alexnet --mode fixed-single-step -s 1 -n alexnet_fixed-single-step_s1

• reversed-single-step
  This mode is reversed order of single-step (blurs second half epochs). usage example:

$ python -u main.py -a alexnet --mode reversed-single-step --reverse_sigma 1 -n alexnet_reversed-single-step_s1

• multi-steps
  This mode blurs images step by step (e.g. every 10 epochs).
  usage example:

$ python main.py --arch alexnet --mode multi-steps -n alexnet_multi-steps

• --blur_val
  This option blurs validation data as well. usage example:

$ python main.py --arch alexnet --mode mix -s 1 --blur_val -n alexnet_mix_lur-val_s1

• --resume [PATH TO SAVED MODEL]
  This option trains your saved model starting from the latest epoch.
  usage example:

$ python main.py --arch alexnet --mode mix -s 1 -n alexnet_mix_s1 --resume ../logs/models/alexnet_mix_s1/checkpoint.pth.tar 

If you want to use multi GPUs trainig, see pytorch imagenet trainning example. e.g.:

$ python main.py -a resnet50 --seed 42 --lr 0.2 --mode normal --epochs 60 -b 512 --dist_url 'tcp://127.0.0.1:10000' --dist_backend 'nccl' --multiprocessing_distributed --world_size 1 --rank 0 -n resnet50_normal_b512

Training scripts and functions are based on pytorch tutorial and pytorch imagenet trainning example.