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k600_slowonly8x8_tpu.yaml
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k600_slowonly8x8_tpu.yaml
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# SlowOnly 8x8 video classification on Kinetics-600.
#
# --experiment_type=video_classification_kinetics600
# Expected accuracy: 77.3% top-1, 93.6% top-5.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
model:
dropout_rate: 0.5
norm_activation:
use_sync_bn: false
backbone:
resnet_3d:
block_specs: !!python/tuple
- temporal_kernel_sizes: !!python/tuple
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- 1
- 1
temporal_strides: 1
use_self_gating: false
- temporal_kernel_sizes: !!python/tuple
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temporal_strides: 1
use_self_gating: false
- temporal_kernel_sizes: !!python/tuple
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temporal_strides: 1
use_self_gating: false
- temporal_kernel_sizes: !!python/tuple
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temporal_strides: 1
use_self_gating: false
model_id: 50
stem_conv_temporal_kernel_size: 1
stem_conv_temporal_stride: 1
stem_pool_temporal_stride: 1
train_data:
name: kinetics600
feature_shape: !!python/tuple
- 8
- 224
- 224
- 3
temporal_stride: 8
global_batch_size: 1024
dtype: 'bfloat16'
shuffle_buffer_size: 1024
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:
name: kinetics600
feature_shape: !!python/tuple
- 8
- 256
- 256
- 3
temporal_stride: 8
num_test_clips: 10
num_test_crops: 3
global_batch_size: 64
dtype: 'bfloat16'
drop_remainder: false
trainer:
optimizer_config:
learning_rate:
cosine:
initial_learning_rate: 0.8
decay_steps: 71488
warmup:
linear:
warmup_steps: 1787
train_steps: 71488
steps_per_loop: 500
summary_interval: 500
validation_interval: 500