我试着在Colab上用这个tutorial制作我自己的对象检测模型。我使用2083训练和263测试示例遵循本教程,训练完成。当我用Tensorboard检查模型时,看起来模型很好地检测到了一些物体。但是,我只得到了-1 mAP和“N/A”与100%像下面的图片。
我使用的是SSD MobileNet V2型号。这些是我的配置文件和other files on github。
model {
ssd {
num_classes: 8
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
feature_extractor {
type: "ssd_mobilenet_v2_keras"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.029999999329447746
}
}
activation: RELU_6
batch_norm {
decay: 0.9700000286102295
center: true
scale: true
epsilon: 0.0010000000474974513
train: true
}
}
override_base_feature_extractor_hyperparams: true
}
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 {
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: 0.9700000286102295
center: true
scale: true
epsilon: 0.0010000000474974513
train: true
}
}
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.800000011920929
kernel_size: 1
box_code_size: 4
apply_sigmoid_to_scores: false
class_prediction_bias_init: -4.599999904632568
}
}
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.20000000298023224
max_scale: 0.949999988079071
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.33329999446868896
}
}
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 {
delta: 1.0
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.75
}
}
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: 4
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.001
total_steps: 25000
warmup_learning_rate: 0.0001
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: "pre-trained-models/ssd_mobilenet_v2_320x320_coco17_tpu-8/checkpoint/ckpt-0"
num_steps: 25000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 10
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "detection"
fine_tune_checkpoint_version: V2
}
train_input_reader {
label_map_path: "annotations/label_map.pbtxt"
tf_record_input_reader {
input_path: "annotations/train_*.record"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1
eval_interval_secs: 30
num_examples: 236 # no of test images
num_visualizations: 10 # no of visualizations for tensorboard
max_num_boxes_to_visualize: 5
visualize_groundtruth_boxes: true
}
eval_input_reader {
label_map_path: "annotations/label_map.pbtxt"
shuffle: true
num_epochs: 1
tf_record_input_reader {
input_path: "annotations/test_*.record"
}
}
我尽力了
1.修改生成tfrecord文件的代码。
1.改变批量大小和步骤数。
1.在label_map. pbtxt中用拉丁字符写入所有内容。
1.检查tfrecord文件是否生成良好。
但是,什么都没变。
并且我发现模型是-1mAP并且在大约100~200步的评估上以100%检测为“N/A”。
1条答案
按热度按时间m1m5dgzv1#
我解决了这个问题。我在数据集中的label_map.pbtxt中设置id。但我必须将id设置为1。
从
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