MicroNetChallenge

MicroNet Challenge CIFAR100 Track : score 0.004169

torch 1.0.1
torchvision 0.2.2.post3
To use these scripts, please first download the cifar100 dataset with torchvision and change the root argument for dataset in the source codes.

Name		Name	Last commit message	Last commit date
Latest commit History 33 Commits
__pycache__		__pycache__
demo		demo
loss		loss
lr_schedular		lr_schedular
model		model
models		models
quantization		quantization
utils		utils
Description.md		Description.md
README.md		README.md
Score.md		Score.md
Test.md		Test.md
Train.md		Train.md
augmentation_transform.py		augmentation_transform.py
autoaug.py		autoaug.py
autoaug_dataset.py		autoaug_dataset.py
cifar.py		cifar.py
flops_utils.py		flops_utils.py
load_sparse_quant_act_quant_weight_merged.py		load_sparse_quant_act_quant_weight_merged.py
score.py		score.py
score.sh		score.sh
sparse_param.py		sparse_param.py
sparse_quant_act_quant_weight_merge.py		sparse_quant_act_quant_weight_merge.py
sparse_quant_param.py		sparse_quant_param.py
test.py		test.py
train_sparse_quant_act_quant_weight_prune_fc_quant_fc_teacher_student.py		train_sparse_quant_act_quant_weight_prune_fc_quant_fc_teacher_student.py
train_sparse_quant_act_quant_weight_prune_fc_teacher_student.py		train_sparse_quant_act_quant_weight_prune_fc_teacher_student.py
train_sparse_quant_act_quant_weight_teacher_student.py		train_sparse_quant_act_quant_weight_teacher_student.py
train_sparse_quant_act_teacher_student.py		train_sparse_quant_act_teacher_student.py
train_sparse_teacher_student.py		train_sparse_teacher_student.py
transform.py		transform.py

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