official/legacy/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b0-gpu.yaml
# Training configuration for EfficientNet-b0 trained on ImageNet on GPUs.
# Takes ~32 minutes per epoch for 8 V100s.
# Reaches ~76.1% within 350 epochs.
# Note: This configuration uses a scaled per-replica batch size based on the number of devices.
runtime:
distribution_strategy: 'mirrored'
num_gpus: 1
train_dataset:
name: 'imagenet2012'
data_dir: null
builder: 'records'
split: 'train'
num_classes: 1000
num_examples: 1281167
batch_size: 32
use_per_replica_batch_size: true
dtype: 'float32'
augmenter:
name: 'autoaugment'
validation_dataset:
name: 'imagenet2012'
data_dir: null
builder: 'records'
split: 'validation'
num_classes: 1000
num_examples: 50000
batch_size: 32
use_per_replica_batch_size: true
dtype: 'float32'
model:
model_params:
model_name: 'efficientnet-b0'
overrides:
num_classes: 1000
batch_norm: 'default'
dtype: 'float32'
activation: 'swish'
optimizer:
name: 'rmsprop'
momentum: 0.9
decay: 0.9
moving_average_decay: 0.0
lookahead: false
learning_rate:
name: 'exponential'
loss:
label_smoothing: 0.1
train:
resume_checkpoint: true
epochs: 500
evaluation:
epochs_between_evals: 1