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# Video classification using MoViNet-A1 backbone on multiple GPUs.
# This configuration is incomplete - Some parameters will be set by model garden Docker.
# --experiment_type=movinet_kinetics600
runtime:
distribution_strategy: 'multi_worker_mirrored'
mixed_precision_dtype: 'bfloat16'
task:
losses:
l2_weight_decay: 0.00003
label_smoothing: 0.1
model:
backbone:
movinet:
model_id: 'a1'
stochastic_depth_drop_rate: 0.2
causal: false
norm_activation:
use_sync_bn: true
dropout_rate: 0.5
activation: 'swish'
train_data:
variant_name: rgb
feature_shape: !!python/tuple
- 32
- 172
- 172
- 3
temporal_stride: 5
random_stride_range: 1
dtype: 'bfloat16'
min_image_size: 192
aug_max_area_ratio: 1.0
aug_max_aspect_ratio: 2.0
aug_min_area_ratio: 0.08
aug_min_aspect_ratio: 0.5
validation_data:
feature_shape: !!python/tuple
- 32
- 172
- 172
- 3
temporal_stride: 5
num_test_clips: 1
num_test_crops: 1
min_image_size: 192
dtype: 'bfloat16'
drop_remainder: false
trainer:
optimizer_config:
learning_rate:
cosine:
initial_learning_rate: 1.8
decay_steps: 85785
warmup:
linear:
warmup_steps: 300
optimizer:
type: 'rmsprop'
rmsprop:
rho: 0.9
momentum: 0.9
epsilon: 1.0
clipnorm: 1.0
steps_per_loop: 500
summary_interval: 500
validation_interval: 500
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