Removes unneeded content of the beta folder.

PiperOrigin-RevId: 437276665

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs152_i224.yaml

-# ResNet-RS-152 ImageNet classification. 82.5% top-1 accuracy.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [224, 224, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 152
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.0
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.25
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 15
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs152_i256.yaml

-# ResNet-RS-152 ImageNet classification. 83.1% top-1 accuracy.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [256, 256, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 152
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.0
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.25
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 15
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs200_i256.yaml

-# ResNet-RS-200 ImageNet classification. 83.5% top-1 accuracy.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [256, 256, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 200
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.1
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.25
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 15
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs270_i256.yaml

-# ResNet-RS-270 ImageNet classification. 83.6% top-1 accuracy.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [256, 256, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 270
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.1
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.25
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 15
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs350_i256.yaml

-# ResNet-RS-350 ImageNet classification. 83.7% top-1 accuracy.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [256, 256, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 350
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.1
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.25
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 15
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs350_i320.yaml

-# ResNet-RS-350 ImageNet classification. 84.2% top-1 accuracy.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [320, 320, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 350
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.1
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.4
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 15
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs420_i320.yaml

-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [320, 320, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 420
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.1
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.4
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 15
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/image_classification/imagenet_resnetrs50_i160.yaml

-# ResNet-RS-50 ImageNet classification. 79.1% top-1 accuracy.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  model:
-    num_classes: 1001
-    input_size: [160, 160, 3]
-    backbone:
-      type: 'resnet'
-      resnet:
-        model_id: 50
-        replace_stem_max_pool: true
-        resnetd_shortcut: true
-        se_ratio: 0.25
-        stem_type: 'v1'
-        stochastic_depth_drop_rate: 0.0
-    norm_activation:
-      activation: 'swish'
-      norm_momentum: 0.0
-      use_sync_bn: false
-    dropout_rate: 0.25
-  losses:
-    l2_weight_decay: 0.00004
-    one_hot: true
-    label_smoothing: 0.1
-  train_data:
-    input_path: 'imagenet-2012-tfrecord/train*'
-    is_training: true
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    aug_type:
-      type: 'randaug'
-      randaug:
-        magnitude: 10
-  validation_data:
-    input_path: 'imagenet-2012-tfrecord/valid*'
-    is_training: false
-    global_batch_size: 4096
-    dtype: 'bfloat16'
-    drop_remainder: false
-trainer:
-  train_steps: 109200
-  validation_steps: 13
-  validation_interval: 312
-  steps_per_loop: 312
-  summary_interval: 312
-  checkpoint_interval: 312
-  optimizer_config:
-    ema:
-      average_decay: 0.9999
-    optimizer:
-      type: 'sgd'
-      sgd:
-        momentum: 0.9
-    learning_rate:
-      type: 'cosine'
-      cosine:
-        initial_learning_rate: 1.6
-        decay_steps: 109200
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 1560

official/vision/beta/configs/experiments/maskrcnn/coco_spinenet143_cascadercnn_tpu.yaml

-# --experiment_type=cascadercnn_spinenet_coco
-# Expect to reach: box mAP: 51.9%, mask mAP: 45.0% on COCO
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  init_checkpoint: null
-  train_data:
-    global_batch_size: 256
-    parser:
-      aug_rand_hflip: true
-      aug_scale_min: 0.1
-      aug_scale_max: 2.5
-  losses:
-    l2_weight_decay: 0.00004
-  model:
-    anchor:
-      anchor_size: 4.0
-      num_scales: 3
-    min_level: 3
-    max_level: 7
-    input_size: [1280, 1280, 3]
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '143'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    detection_head:
-      cascade_class_ensemble: true
-      class_agnostic_bbox_pred: true
-    rpn_head:
-      num_convs: 2
-      num_filters: 256
-    roi_sampler:
-      cascade_iou_thresholds: [0.7]
-      foreground_iou_threshold: 0.6
-    norm_activation:
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-      activation: 'swish'
-    detection_generator:
-      pre_nms_top_k: 1000
-trainer:
-  train_steps: 231000
-  optimizer_config:
-    learning_rate:
-      type: 'stepwise'
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 2000
-        warmup_learning_rate: 0.0067

official/vision/beta/configs/experiments/maskrcnn/coco_spinenet143_mrcnn_tpu.yaml

-# Expect to reach: box mAP: 49.3%, mask mAP: 43.4% on COCO
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  init_checkpoint: null
-  train_data:
-    global_batch_size: 256
-    parser:
-      aug_rand_hflip: true
-      aug_scale_min: 0.1
-      aug_scale_max: 2.0
-  losses:
-    l2_weight_decay: 0.00004
-  model:
-    anchor:
-      anchor_size: 4.0
-      num_scales: 3
-    min_level: 3
-    max_level: 7
-    input_size: [1280, 1280, 3]
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '143'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    norm_activation:
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-    detection_generator:
-      pre_nms_top_k: 1000
-trainer:
-  train_steps: 231000
-  optimizer_config:
-    learning_rate:
-      type: 'stepwise'
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 2000
-        warmup_learning_rate: 0.0067

official/vision/beta/configs/experiments/maskrcnn/coco_spinenet49_cascadercnn_tpu.yaml

-# --experiment_type=cascadercnn_spinenet_coco
-# Expect to reach: box mAP: 46.4%, mask mAP: 40.0% on COCO
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  init_checkpoint: null
-  train_data:
-    global_batch_size: 256
-    parser:
-      aug_rand_hflip: true
-      aug_scale_min: 0.1
-      aug_scale_max: 2.0
-  losses:
-    l2_weight_decay: 0.00004
-  model:
-    anchor:
-      anchor_size: 3.0
-      num_scales: 3
-    min_level: 3
-    max_level: 7
-    input_size: [640, 640, 3]
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '49'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    detection_head:
-      cascade_class_ensemble: true
-      class_agnostic_bbox_pred: true
-    rpn_head:
-      num_convs: 2
-      num_filters: 256
-    roi_sampler:
-      cascade_iou_thresholds: [0.7]
-      foreground_iou_threshold: 0.6
-    norm_activation:
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-      activation: 'swish'
-    detection_generator:
-      pre_nms_top_k: 1000
-trainer:
-  train_steps: 231000
-  optimizer_config:
-    learning_rate:
-      type: 'stepwise'
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 2000
-        warmup_learning_rate: 0.0067

official/vision/beta/configs/experiments/maskrcnn/coco_spinenet49_mrcnn_tpu.yaml

-# Expect to reach: box mAP: 43.2%, mask mAP: 38.3% on COCO
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  init_checkpoint: null
-  train_data:
-    global_batch_size: 256
-    parser:
-      aug_rand_hflip: true
-      aug_scale_min: 0.1
-      aug_scale_max: 2.0
-  losses:
-    l2_weight_decay: 0.00004
-  model:
-    anchor:
-      anchor_size: 3.0
-      num_scales: 3
-    min_level: 3
-    max_level: 7
-    input_size: [640, 640, 3]
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '49'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    norm_activation:
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-    detection_generator:
-      pre_nms_top_k: 1000
-trainer:
-  train_steps: 231000
-  optimizer_config:
-    learning_rate:
-      type: 'stepwise'
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 2000
-        warmup_learning_rate: 0.0067

official/vision/beta/configs/experiments/maskrcnn/coco_spinenet96_cascadercnn_tpu.yaml

-# --experiment_type=cascadercnn_spinenet_coco
-# Expect to reach: box mAP: 51.9%, mask mAP: 45.0% on COCO
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  init_checkpoint: null
-  train_data:
-    global_batch_size: 256
-    parser:
-      aug_rand_hflip: true
-      aug_scale_min: 0.1
-      aug_scale_max: 2.5
-  losses:
-    l2_weight_decay: 0.00004
-  model:
-    anchor:
-      anchor_size: 4.0
-      num_scales: 3
-    min_level: 3
-    max_level: 7
-    input_size: [1024, 1024, 3]
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '96'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    detection_head:
-      cascade_class_ensemble: true
-      class_agnostic_bbox_pred: true
-    rpn_head:
-      num_convs: 2
-      num_filters: 256
-    roi_sampler:
-      cascade_iou_thresholds: [0.7]
-      foreground_iou_threshold: 0.6
-    norm_activation:
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-      activation: 'swish'
-    detection_generator:
-      pre_nms_top_k: 1000
-trainer:
-  train_steps: 231000
-  optimizer_config:
-    learning_rate:
-      type: 'stepwise'
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 2000
-        warmup_learning_rate: 0.0067

official/vision/beta/configs/experiments/maskrcnn/coco_spinenet96_mrcnn_tpu.yaml

-# Expect to reach: box mAP: 48.1%, mask mAP: 42.4% on COCO
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  init_checkpoint: null
-  train_data:
-    global_batch_size: 256
-    parser:
-      aug_rand_hflip: true
-      aug_scale_min: 0.1
-      aug_scale_max: 2.0
-  losses:
-    l2_weight_decay: 0.00004
-  model:
-    anchor:
-      anchor_size: 3.0
-      num_scales: 3
-    min_level: 3
-    max_level: 7
-    input_size: [1024, 1024, 3]
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '96'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    norm_activation:
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-    detection_generator:
-      pre_nms_top_k: 1000
-trainer:
-  train_steps: 231000
-  optimizer_config:
-    learning_rate:
-      type: 'stepwise'
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 2000
-        warmup_learning_rate: 0.0067

official/vision/beta/configs/experiments/maskrcnn/r50fpn_640_coco_scratch_tpu4x4.yaml

-# Expect to reach: box mAP: 42.3%, mask mAP: 37.6% on COCO
-task:
-  init_checkpoint: null
-  train_data:
-    global_batch_size: 256
-    parser:
-      aug_rand_hflip: true
-      aug_scale_min: 0.5
-      aug_scale_max: 2.0
-  losses:
-    l2_weight_decay: 0.00008
-  model:
-    anchor:
-      anchor_size: 3.0
-    min_level: 3
-    max_level: 7
-    input_size: [640, 640, 3]
-    norm_activation:
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-    detection_generator:
-      pre_nms_top_k: 1000
-trainer:
-  train_steps: 162050
-  optimizer_config:
-    learning_rate:
-      type: 'stepwise'
-      stepwise:
-        boundaries: [148160, 157420]
-        values: [0.32, 0.032, 0.0032]
-    warmup:
-      type: 'linear'
-      linear:
-        warmup_steps: 2000
-        warmup_learning_rate: 0.0067

official/vision/beta/configs/experiments/retinanet/coco_mobiledetcpu_tpu.yaml

-# --experiment_type=retinanet_mobile_coco
-# COCO AP 27.0%
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  losses:
-    l2_weight_decay: 3.0e-05
-  model:
-    anchor:
-      anchor_size: 3
-      aspect_ratios: [0.5, 1.0, 2.0]
-      num_scales: 3
-    backbone:
-      mobilenet:
-        model_id: 'MobileDetCPU'
-        filter_size_scale: 1.0
-      type: 'mobiledet'
-    decoder:
-      type: 'fpn'
-      fpn:
-        num_filters: 128
-        use_separable_conv: true
-    head:
-      num_convs: 4
-      num_filters: 128
-      use_separable_conv: true
-    input_size: [320 320, 3]
-    max_level: 6
-    min_level: 3
-    norm_activation:
-      activation: 'relu6'
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-  train_data:
-    dtype: 'bfloat16'
-    global_batch_size: 256
-    is_training: true
-    parser:
-      aug_rand_hflip: true
-      aug_scale_max: 2.0
-      aug_scale_min: 0.5
-  validation_data:
-    dtype: 'bfloat16'
-    global_batch_size: 8
-    is_training: false
-trainer:
-  optimizer_config:
-    learning_rate:
-      stepwise:
-        boundaries: [263340, 272580]
-        values: [0.32, 0.032, 0.0032]
-      type: 'stepwise'
-    warmup:
-      linear:
-        warmup_learning_rate: 0.0067
-        warmup_steps: 2000
-  steps_per_loop: 462
-  train_steps: 277200
-  validation_interval: 462
-  validation_steps: 625

official/vision/beta/configs/experiments/retinanet/coco_mobilenetv2_tpu.yaml

-# --experiment_type=retinanet_mobile_coco
-# COCO AP 23.5%
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  losses:
-    l2_weight_decay: 3.0e-05
-  model:
-    anchor:
-      anchor_size: 3
-      aspect_ratios: [0.5, 1.0, 2.0]
-      num_scales: 3
-    backbone:
-      mobilenet:
-        model_id: 'MobileNetV2'
-        filter_size_scale: 1.0
-      type: 'mobilenet'
-    decoder:
-      type: 'fpn'
-      fpn:
-        num_filters: 128
-        use_separable_conv: true
-    head:
-      num_convs: 4
-      num_filters: 128
-      use_separable_conv: true
-    input_size: [256, 256, 3]
-    max_level: 7
-    min_level: 3
-    norm_activation:
-      activation: 'relu6'
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-  train_data:
-    dtype: 'bfloat16'
-    global_batch_size: 256
-    is_training: true
-    parser:
-      aug_rand_hflip: true
-      aug_scale_max: 2.0
-      aug_scale_min: 0.5
-  validation_data:
-    dtype: 'bfloat16'
-    global_batch_size: 8
-    is_training: false
-trainer:
-  optimizer_config:
-    learning_rate:
-      stepwise:
-        boundaries: [263340, 272580]
-        values: [0.32, 0.032, 0.0032]
-      type: 'stepwise'
-    warmup:
-      linear:
-        warmup_learning_rate: 0.0067
-        warmup_steps: 2000
-  steps_per_loop: 462
-  train_steps: 277200
-  validation_interval: 462
-  validation_steps: 625

official/vision/beta/configs/experiments/retinanet/coco_spinenet143_tpu.yaml

-# SpineNet-143 COCO detection with protocal C config. Expecting 50.0% AP.
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  losses:
-    l2_weight_decay: 4.0e-05
-  model:
-    anchor:
-      anchor_size: 4
-      aspect_ratios: [0.5, 1.0, 2.0]
-      num_scales: 3
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '143'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    head:
-      num_convs: 4
-      num_filters: 256
-    input_size: [1280, 1280, 3]
-    max_level: 7
-    min_level: 3
-    norm_activation:
-      activation: 'swish'
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-  train_data:
-    dtype: 'bfloat16'
-    global_batch_size: 256
-    is_training: true
-    parser:
-      aug_rand_hflip: true
-      aug_scale_max: 2.0
-      aug_scale_min: 0.1
-  validation_data:
-    dtype: 'bfloat16'
-    global_batch_size: 8
-    is_training: false
-trainer:
-  checkpoint_interval: 462
-  optimizer_config:
-    learning_rate:
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-      type: 'stepwise'
-    warmup:
-      linear:
-        warmup_learning_rate: 0.0067
-        warmup_steps: 2000
-  steps_per_loop: 462
-  train_steps: 231000
-  validation_interval: 462
-  validation_steps: 625

official/vision/beta/configs/experiments/retinanet/coco_spinenet190_tpu.yaml

-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  losses:
-    l2_weight_decay: 4.0e-05
-  model:
-    anchor:
-      anchor_size: 4
-      aspect_ratios: [0.5, 1.0, 2.0]
-      num_scales: 3
-    backbone:
-      spinenet:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '190'
-      type: 'spinenet'
-    decoder:
-      type: 'identity'
-    head:
-      num_convs: 7
-      num_filters: 512
-    input_size: [1280, 1280, 3]
-    max_level: 7
-    min_level: 3
-    norm_activation:
-      activation: 'swish'
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-  train_data:
-    dtype: 'bfloat16'
-    global_batch_size: 256
-    is_training: true
-    parser:
-      aug_rand_hflip: true
-      aug_scale_max: 2.0
-      aug_scale_min: 0.1
-  validation_data:
-    dtype: 'bfloat16'
-    global_batch_size: 8
-    is_training: false
-trainer:
-  checkpoint_interval: 462
-  optimizer_config:
-    learning_rate:
-      stepwise:
-        boundaries: [219450, 226380]
-        values: [0.32, 0.032, 0.0032]
-      type: 'stepwise'
-    warmup:
-      linear:
-        warmup_learning_rate: 0.0067
-        warmup_steps: 2000
-  steps_per_loop: 462
-  train_steps: 231000
-  validation_interval: 462
-  validation_steps: 625

official/vision/beta/configs/experiments/retinanet/coco_spinenet49_mobile_tpu.yaml

-# --experiment_type=retinanet_mobile_coco
-runtime:
-  distribution_strategy: 'tpu'
-  mixed_precision_dtype: 'bfloat16'
-task:
-  losses:
-    l2_weight_decay: 3.0e-05
-  model:
-    anchor:
-      anchor_size: 3
-      aspect_ratios: [0.5, 1.0, 2.0]
-      num_scales: 3
-    backbone:
-      spinenet_mobile:
-        stochastic_depth_drop_rate: 0.2
-        model_id: '49'
-        se_ratio: 0.2
-      type: 'spinenet_mobile'
-    decoder:
-      type: 'identity'
-    head:
-      num_convs: 4
-      num_filters: 48
-      use_separable_conv: true
-    input_size: [384, 384, 3]
-    max_level: 7
-    min_level: 3
-    norm_activation:
-      activation: 'swish'
-      norm_epsilon: 0.001
-      norm_momentum: 0.99
-      use_sync_bn: true
-  train_data:
-    dtype: 'bfloat16'
-    global_batch_size: 256
-    is_training: true
-    parser:
-      aug_rand_hflip: true
-      aug_scale_max: 2.0
-      aug_scale_min: 0.5
-  validation_data:
-    dtype: 'bfloat16'
-    global_batch_size: 8
-    is_training: false
-trainer:
-  checkpoint_interval: 462
-  optimizer_config:
-    learning_rate:
-      stepwise:
-        boundaries: [263340, 272580]
-        values: [0.32, 0.032, 0.0032]
-      type: 'stepwise'
-    warmup:
-      linear:
-        warmup_learning_rate: 0.0067
-        warmup_steps: 2000
-  steps_per_loop: 462
-  train_steps: 277200
-  validation_interval: 462
-  validation_steps: 625