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Commit 6cdab86

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‎object-detection.pbtxt

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item {
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id: 1
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name: 'up'
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}
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item {
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id:2
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name:"down"
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}
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item {
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id:3
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name:"left"
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}
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item {
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id:4
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name:"right"
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}

‎ssd_mobilenet_v1_pets.config

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# SSD with Mobilenet v1, configured for Oxford-IIIT Pets Dataset.
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# Users should configure the fine_tune_checkpoint field in the train config as
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# well as the label_map_path and input_path fields in the train_input_reader and
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# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
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# should be configured.
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model {
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ssd {
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num_classes: 4
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box_coder {
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faster_rcnn_box_coder {
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y_scale: 10.0
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x_scale: 10.0
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height_scale: 5.0
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width_scale: 5.0
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}
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}
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matcher {
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argmax_matcher {
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matched_threshold: 0.5
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unmatched_threshold: 0.5
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ignore_thresholds: false
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negatives_lower_than_unmatched: true
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force_match_for_each_row: true
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}
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}
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similarity_calculator {
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iou_similarity {
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}
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}
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anchor_generator {
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ssd_anchor_generator {
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num_layers: 6
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min_scale: 0.2
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max_scale: 0.95
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aspect_ratios: 1.0
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aspect_ratios: 2.0
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aspect_ratios: 0.5
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aspect_ratios: 3.0
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aspect_ratios: 0.3333
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}
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}
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image_resizer {
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fixed_shape_resizer {
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height: 300
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width: 300
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}
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}
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box_predictor {
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convolutional_box_predictor {
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min_depth: 0
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max_depth: 0
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num_layers_before_predictor: 0
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use_dropout: false
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dropout_keep_probability: 0.8
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kernel_size: 1
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box_code_size: 4
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apply_sigmoid_to_scores: false
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conv_hyperparams {
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activation: RELU_6,
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regularizer {
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l2_regularizer {
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weight: 0.00004
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}
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}
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initializer {
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truncated_normal_initializer {
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stddev: 0.03
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mean: 0.0
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}
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}
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batch_norm {
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train: true,
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scale: true,
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center: true,
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decay: 0.9997,
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epsilon: 0.001,
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}
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}
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}
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}
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feature_extractor {
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type: 'ssd_mobilenet_v1'
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min_depth: 16
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depth_multiplier: 1.0
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conv_hyperparams {
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activation: RELU_6,
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regularizer {
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l2_regularizer {
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weight: 0.00004
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}
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}
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initializer {
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truncated_normal_initializer {
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stddev: 0.03
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mean: 0.0
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}
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}
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batch_norm {
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train: true,
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scale: true,
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center: true,
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decay: 0.9997,
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epsilon: 0.001,
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}
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}
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}
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loss {
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classification_loss {
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weighted_sigmoid {
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anchorwise_output: true
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}
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}
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localization_loss {
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weighted_smooth_l1 {
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anchorwise_output: true
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}
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}
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hard_example_miner {
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num_hard_examples: 3000
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iou_threshold: 0.99
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loss_type: CLASSIFICATION
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max_negatives_per_positive: 3
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min_negatives_per_image: 0
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}
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classification_weight: 1.0
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localization_weight: 1.0
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}
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normalize_loss_by_num_matches: true
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post_processing {
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batch_non_max_suppression {
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score_threshold: 1e-8
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iou_threshold: 0.6
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max_detections_per_class: 100
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max_total_detections: 100
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}
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score_converter: SIGMOID
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}
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}
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}
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train_config: {
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batch_size: 16
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optimizer {
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rms_prop_optimizer: {
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learning_rate: {
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exponential_decay_learning_rate {
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initial_learning_rate: 0.004
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decay_steps: 800720
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decay_factor: 0.95
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}
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}
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momentum_optimizer_value: 0.9
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decay: 0.9
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epsilon: 1.0
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}
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}
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fine_tune_checkpoint: "D:/models/research/images/ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"
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from_detection_checkpoint: true
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# Note: The below line limits the training process to 200K steps, which we
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# empirically found to be sufficient enough to train the pets dataset. This
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# effectively bypasses the learning rate schedule (the learning rate will
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# never decay). Remove the below line to train indefinitely.
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num_steps: 200000
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data_augmentation_options {
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random_horizontal_flip {
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}
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}
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data_augmentation_options {
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ssd_random_crop {
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}
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}
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}
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train_input_reader: {
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tf_record_input_reader {
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input_path: "D:/models/research/images/data/train.record"
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}
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label_map_path: "D:/models/research/images/data/object-detection.pbtxt"
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}
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eval_config: {
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num_examples: 2000
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# Note: The below line limits the evaluation process to 10 evaluations.
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# Remove the below line to evaluate indefinitely.
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max_evals: 10
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}
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eval_input_reader: {
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tf_record_input_reader {
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input_path: "D:/models/research/images/data/test.record"
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}
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label_map_path: "D:/models/research/images/data/object-detection.pbtxt"
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shuffle: false
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num_readers: 1
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}

‎test_labels.csv

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,filename,width,height,class,xmin,ymin,xmax,ymax
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0,101.jpg,640,480,down,180,21,398,265
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1,115.jpg,640,480,down,211,70,398,349
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2,116.jpg,640,480,down,180,64,386,357
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3,117.jpg,640,480,down,167,58,375,367
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4,118.jpg,640,480,down,165,61,374,372
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5,119.jpg,640,480,down,162,62,374,375
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6,174.jpg,640,480,down,365,56,603,430
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7,194.jpg,640,480,down,254,72,480,384
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8,201.jpg,640,480,down,239,67,476,378
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9,203.jpg,640,480,down,217,70,459,383
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10,205.jpg,640,480,down,183,76,425,387
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11,206.jpg,640,480,down,158,75,397,391
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12,22.jpg,640,480,down,170,136,343,395
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13,23_23_23_23.jpg,640,480,up,85,174,293,473
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14,24_24_24_24.jpg,640,480,up,73,164,279,455
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15,256_256_256_256.jpg,640,480,up,359,171,587,445
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16,257_257_257_257.jpg,640,480,up,370,173,567,455
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17,259.jpg,640,480,down,36,98,269,458
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18,25_25_25_25.jpg,640,480,up,67,162,277,479
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19,272.jpg,640,480,down,220,100,450,475
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20,277.jpg,640,480,down,233,79,478,455
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21,28.jpg,640,480,down,116,142,288,389
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22,416_416_416_416.jpg,640,480,up,185,149,461,454
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23,417_417_417_417.jpg,640,480,up,121,121,482,453
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24,418_418_418_418.jpg,640,480,up,195,148,505,444
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25,43.jpg,640,480,down,272,142,485,396
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26,45.jpg,640,480,down,269,144,485,394
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27,49.jpg,640,480,down,286,146,505,398
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28,53.jpg,640,480,down,294,146,522,401
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29,59_59_59_59.jpg,640,480,up,100,185,348,475
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30,61_61_61_61.jpg,640,480,up,69,183,320,467
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31,62_62_62_62.jpg,640,480,up,49,177,329,440
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32,68.jpg,640,480,down,248,131,455,414
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33,74_74_74_74.jpg,640,480,up,39,176,317,460
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34,75.jpg,640,480,down,199,119,381,427
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35,75_75_75_75.jpg,640,480,up,39,183,281,466
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36,77.jpg,640,480,down,205,121,382,430
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37,84_84_84_84.jpg,640,480,up,7,186,267,479
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38,85_85_85_85.jpg,640,480,up,8,180,274,480
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39,WIN_20190209_11_00_28_Pro.jpg,1280,720,right,583,463,928,674
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40,WIN_20190209_11_00_31_Pro.jpg,1280,720,right,328,362,643,580
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41,WIN_20190209_11_00_49_Pro.jpg,1280,720,right,370,65,681,304
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42,WIN_20190209_11_00_51_Pro.jpg,1280,720,right,328,164,678,388
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43,WIN_20190209_11_01_00_Pro.jpg,1280,720,right,336,272,556,437
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44,WIN_20190209_11_01_02_Pro.jpg,1280,720,right,321,301,542,436
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45,WIN_20190209_11_01_04_Pro.jpg,1280,720,right,318,276,520,425
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46,WIN_20190209_11_01_06_Pro.jpg,1280,720,right,504,287,699,405
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47,WIN_20190209_11_01_08_Pro.jpg,1280,720,right,561,205,734,361
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48,WIN_20190209_11_01_12_Pro.jpg,1280,720,right,83,203,298,361
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49,WIN_20190209_11_01_17_Pro.jpg,1280,720,right,679,269,888,412
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50,WIN_20190209_11_01_20_Pro.jpg,1280,720,right,152,267,342,427
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65,WIN_20190209_11_18_33_Pro.jpg,1280,720,left,120,293,327,431
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70,WIN_20190209_11_18_45_Pro.jpg,1280,720,left,406,333,590,450
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86,WIN_20190209_11_19_43_Pro.jpg,1280,720,left,534,152,692,280

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