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- # SSD with Mobilenet v1 configuration for MSCOCO 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 {
- ssd {
- num_classes: 1
- box_coder {
- faster_rcnn_box_coder {
- y_scale: 10.0
- x_scale: 10.0
- height_scale: 5.0
- width_scale: 5.0
- }
- }
- matcher {
- argmax_matcher {
- matched_threshold: 0.5
- unmatched_threshold: 0.5
- ignore_thresholds: false
- negatives_lower_than_unmatched: true
- force_match_for_each_row: true
- }
- }
- similarity_calculator {
- iou_similarity {
- }
- }
- anchor_generator {
- ssd_anchor_generator {
- num_layers: 6
- min_scale: 0.2
- max_scale: 0.95
- aspect_ratios: 1.0
- aspect_ratios: 2.0
- aspect_ratios: 0.5
- aspect_ratios: 3.0
- aspect_ratios: 0.3333
- }
- }
- image_resizer {
- fixed_shape_resizer {
- height: 300
- width: 300
- }
- }
- box_predictor {
- convolutional_box_predictor {
- min_depth: 0
- max_depth: 0
- num_layers_before_predictor: 0
- use_dropout: false
- dropout_keep_probability: 0.8
- kernel_size: 1
- box_code_size: 4
- apply_sigmoid_to_scores: false
- conv_hyperparams {
- activation: RELU_6,
- regularizer {
- l2_regularizer {
- weight: 0.00004
- }
- }
- initializer {
- truncated_normal_initializer {
- stddev: 0.03
- mean: 0.0
- }
- }
- batch_norm {
- train: true,
- scale: true,
- center: true,
- decay: 0.9997,
- epsilon: 0.001,
- }
- }
- }
- }
- feature_extractor {
- type: 'ssd_mobilenet_v1'
- min_depth: 16
- depth_multiplier: 1.0
- conv_hyperparams {
- activation: RELU_6,
- regularizer {
- l2_regularizer {
- weight: 0.00004
- }
- }
- initializer {
- truncated_normal_initializer {
- stddev: 0.03
- mean: 0.0
- }
- }
- batch_norm {
- train: true,
- scale: true,
- center: true,
- decay: 0.9997,
- epsilon: 0.001,
- }
- }
- }
- loss {
- classification_loss {
- weighted_sigmoid {
- anchorwise_output: true
- }
- }
- localization_loss {
- weighted_smooth_l1 {
- anchorwise_output: true
- }
- }
- hard_example_miner {
- num_hard_examples: 3000
- iou_threshold: 0.99
- loss_type: CLASSIFICATION
- max_negatives_per_positive: 3
- min_negatives_per_image: 0
- }
- classification_weight: 1.0
- localization_weight: 1.0
- }
- normalize_loss_by_num_matches: true
- post_processing {
- batch_non_max_suppression {
- score_threshold: 1e-8
- iou_threshold: 0.6
- max_detections_per_class: 100
- max_total_detections: 100
- }
- score_converter: SIGMOID
- }
- }
- }
- train_config: {
- batch_size: 2
- optimizer {
- rms_prop_optimizer: {
- learning_rate: {
- exponential_decay_learning_rate {
- initial_learning_rate: 0.004
- decay_steps: 800720
- decay_factor: 0.95
- }
- }
- momentum_optimizer_value: 0.9
- decay: 0.9
- epsilon: 1.0
- }
- }
- fine_tune_checkpoint: "ssd_mobilenet_v1_coco_2017_11_17/model.ckpt"
- from_detection_checkpoint: true
- # Note: The below line limits the training process to 200K steps, which we
- # empirically found to be sufficient enough to train the pets dataset. This
- # effectively bypasses the learning rate schedule (the learning rate will
- # never decay). Remove the below line to train indefinitely.
- num_steps: 200000
- data_augmentation_options {
- random_horizontal_flip {
- }
- }
- data_augmentation_options {
- ssd_random_crop {
- }
- }
- }
- train_input_reader: {
- tf_record_input_reader {
- input_path: "data/train.record"
- }
- label_map_path: "data/object-detection.pbtxt"
- }
- eval_config: {
- 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: "data/test.record"
- }
- label_map_path: "data/object-detection.pbtxt"
- shuffle: false
- num_readers: 1
- num_epochs: 1
- }
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