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add open image dataset config.
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tombstone committed Nov 18, 2017
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# Faster R-CNN with Inception Resnet v2, Atrous version;
# Configured for Open Images Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
faster_rcnn {
num_classes: 546
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_inception_resnet_v2'
first_stage_features_stride: 8
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 8
width_stride: 8
}
}
first_stage_atrous_rate: 2
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 17
maxpool_kernel_size: 1
maxpool_stride: 1
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}

train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.00006
schedule {
step: 0
learning_rate: .00006
}
schedule {
step: 6000000
learning_rate: .000006
}
schedule {
step: 7000000
learning_rate: .0000006
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
# Note: The below line limits the training process to 800K steps, which we
# empirically found to be sufficient enough to train the Open Images dataset.
# This effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 8000000
data_augmentation_options {
random_horizontal_flip {
}
}
}

train_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_train.record"
}
label_map_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_label_map.pbtxt"
}

eval_config: {
metrics_set: "open_images_metrics"
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}

eval_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_val.record"
}
label_map_path: "PATH_TO_BE_CONFIGURED/oid_bbox_trainable_label_map.pbtxt"
shuffle: false
num_readers: 1
}

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