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tensorflow - 迁移学习如何与 TensorFlow 对象检测 API 配合使用？

问题描述

我正在使用 tensorflow 对象检测 api 在图像数据集上训练模型（MobileNet V2 fpn lite）。我不太明白使用什么类型的迁移学习，因为我无法在配置文件中找到与它特别相关的信息（例如冻结层的数量）。

这是我的配置文件：

model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 320
width: 320
}
}
feature_extractor {
type: "ssd_mobilenet_v2_fpn_keras"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019165
scale: true
epsilon: 0.0010000000474974513
}
}
use_depthwise: true
override_base_feature_extractor_hyperparams: true
fpn {
min_level: 3
max_level: 7
additional_layer_depth: 128
}
}
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
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019165
scale: true
epsilon: 0.0010000000474974513
}
}
depth: 128
num_layers_before_predictor: 4
kernel_size: 3
class_prediction_bias_init: -4.599999904632568
share_prediction_tower: true
use_depthwise: true
}
}
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
scales_per_octave: 2
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 9.99999993922529e-09
iou_threshold: 0.6000000238418579
max_detections_per_class: 100
max_total_detections: 100
use_static_shapes: false
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.25
}
}
classification_weight: 1.0
localization_weight: 1.0
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
}
}
train_config {
batch_size: 35 #128
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_crop_image {
min_object_covered: 0.0
min_aspect_ratio: 0.75
max_aspect_ratio: 3.0
min_area: 0.75
max_area: 1.0
overlap_thresh: 0.0
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.03 #0.07999999821186066
total_steps: 50000
warmup_learning_rate: 0.01 #0.026666000485420227
warmup_steps: 1000
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: 'path_to/pre-trained-models/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0'
num_steps: 50000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "detection"
fine_tune_checkpoint_version: V2
}
train_input_reader {
label_map_path: 'path_to/data/object_detection.pbtxt'
tf_record_input_reader {
input_path: 'path_to/data/train.record'
}
}
eval_config {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: 'path_to/data/object_detection.pbtxt'
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: 'path_to/data/test.record'
}
}

你知道迁移学习如何与 tensorflow 对象检测 api 一起工作吗？我们可以改变一些参数来调整迁移学习吗？谢谢！

以下是相关问题：Tensorflow Object detection API with transfer learning for custom classes - 所有层权重是否都使用默认配置更新？

标签： tensorflowobject-detection-apitransfer-learning

解决方案

如果模型文件夹中有检查点，则权重将根据检查点中保存的权重值进行初始化，您将获得迁移学习！但在这种情况下，您使用预训练模型的所有层

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