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cifar100_ls1_kd.yaml
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cifar100_ls1_kd.yaml
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# Validation set evaluation metrics:
# Top-1 Accuracy: 71.5%
# Top-5 Accuracy: 92.0%
seed: null
environment:
platform: local
ngpus: 1
cuda:
cudnn_deterministic: false
cudnn_benchmark: true
data:
dataset_path: data/cifar100/
train_batch_size: 128
test_batch_size: 100
workers: 16
model:
architecture: resnet
loss: cross_entropy
arch_config:
moving_average_mode: 'off'
moving_average_momentum: 0.99
block: xnor
layer0:
n_in_channels: 64
kernel_size: 3
stride: 1
padding: 1
bias: false
maxpool:
type: identity
layer1:
x_quant: ls-1
w_quant: ls-1
clamp:
kind: symmetric
alpha: 2
double_shortcut: true
layer2:
x_quant: ls-1
w_quant: ls-1
clamp:
kind: symmetric
alpha: 2
double_shortcut: true
layer3:
x_quant: ls-1
w_quant: ls-1
clamp:
kind: symmetric
alpha: 2
double_shortcut: true
layer4:
x_quant: ls-1
w_quant: ls-1
clamp:
kind: symmetric
alpha: 2
double_shortcut: true
nonlins: ['relu', 'relu']
num_blocks: [2, 2, 2, 2]
output_classes: 100
kd_config:
teacher_config_path: experiments/cifar100-teacher/config.yaml
teacher_checkpoint_path: experiments/cifar100-teacher/checkpoints/checkpoint_200.pt
freeze_teacher: true
train_mode: true
criterion_config:
temperature: 5
teacher_correction: true
optimization:
epochs: 350
optimizer:
algorithm: adam
lr: 0.0002
weight_decay: 0.0
lr_scheduler:
scheduler: multi_step_lr
milestones:
- 150
- 250
- 320
gamma: 0.1
log:
level: INFO
interval: 100
tensorboard: true
tensorboard_root: runs/
root_experiments_dir: experiments/
save_model_freq: 80