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  1. .gitattributes +8 -0
  2. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/checkpoint/checkpoint +16 -0
  3. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/checkpoint/ckpt-15.data-00000-of-00001 +3 -0
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  18. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/detect.tflite +3 -0
  19. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/labelmap.pbtxt +15 -0
  20. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/labelmap.txt +3 -0
  21. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/pipeline_file.config +201 -0
  22. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/saved_model/saved_model.pb +3 -0
  23. TF-object_detection/saved_model/method_2_lite_20k_enhance_coco/saved_model/variables/variables.data-00000-of-00001 +3 -0
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+ item {
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+ id: 1
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+ item {
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+ id: 2
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+ name: 'dining table'
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+ item {
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+ # SSD with Mobilenet v2 FPN-lite (go/fpn-lite) feature extractor, shared box
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+ # predictor and focal loss (a mobile version of Retinanet).
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+ # Retinanet: see Lin et al, https://arxiv.org/abs/1708.02002
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+ # Trained on COCO, initialized from Imagenet classification checkpoint
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+ # Train on TPU-8
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+ #
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+ # Achieves 22.2 mAP on COCO17 Val
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+
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+ model {
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+ ssd {
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+ inplace_batchnorm_update: true
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+ freeze_batchnorm: false
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+ encode_background_as_zeros: true
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+ anchor_generator {
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+ multiscale_anchor_generator {
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+ min_level: 3
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+ max_level: 7
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+ anchor_scale: 4.0
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+ aspect_ratios: [1.0, 2.0, 0.5]
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+ }
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+ box_predictor {
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+ weight_shared_convolutional_box_predictor {
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+ depth: 128
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+ class_prediction_bias_init: -4.6
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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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+ initializer {
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+ }
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+ alpha: 0.25
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+ gamma: 2.0
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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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+ normalize_loc_loss_by_codesize: 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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+ train_config: {
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+ fine_tune_checkpoint_version: V2
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+ fine_tune_checkpoint: "/content/models/mymodel/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0"
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+ num_steps: 20000
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+ data_augmentation_options {
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+ random_horizontal_flip {
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+ min_aspect_ratio: 0.75
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+ max_aspect_ratio: 3.0
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+ min_area: 0.75
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+ max_area: 1.0
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+ overlap_thresh: 0.0
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+ }
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+ }
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+ momentum_optimizer: {
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+ learning_rate: {
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+ cosine_decay_learning_rate {
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+ learning_rate_base: .08
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+ total_steps: 50000
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+ warmup_learning_rate: .026666
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+ warmup_steps: 1000
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+ }
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+ momentum_optimizer_value: 0.9
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+ use_moving_average: false
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+ }
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+ max_number_of_boxes: 100
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+ unpad_groundtruth_tensors: false
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+ }
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+
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+ train_input_reader: {
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+ label_map_path: "/content/labelmap.pbtxt"
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+ tf_record_input_reader {
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+ input_path: "/content/train.tfrecord"
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+ eval_config: {
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+ metrics_set: "coco_detection_metrics"
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+ use_moving_averages: false
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+ num_epochs: 1
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+ tf_record_input_reader {
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+ input_path: "/content/val.tfrecord"
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