Tensorflow 1.12.0
我目前正在尝试用我的数据集训练SSD_Mobilenet_V1_pnp模型(用COCO预训练(。我的数据集有490张图像用于训练，210张用于评估，23类
label_map.pbtext:
项目{id:1名称："a"}项目{id:2名称：'b'}
pipeline.config:
model {
ssd {
num_classes: 24
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
feature_extractor {
type: "ssd_mobilenet_v1_ppn"
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.00999999977648
}
}
activation: RELU_6
batch_norm {
decay: 0.97000002861
center: true
scale: true
epsilon: 0.0010000000475
}
}
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 {
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.00999999977648
}
}
activation: RELU_6
batch_norm {
decay: 0.97000002861
center: true
scale: true
epsilon: 0.0010000000475
train: true
}
}
depth: 512
num_layers_before_predictor: 1
kernel_size: 1
class_prediction_bias_init: -4.59999990463
share_prediction_tower: true
}
}
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.15000000596
max_scale: 0.949999988079
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.333299994469
reduce_boxes_in_lowest_layer: false
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 0.300000011921
iou_threshold: 0.600000023842
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.75
}
}
classification_weight: 1.0
localization_weight: 1.5
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
}
}
train_config {
batch_size: 512
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: 0.699999988079
total_steps: 50000
warmup_learning_rate: 0.13330000639
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.899999976158
}
use_moving_average: false
}
fine_tune_checkpoint: "model.ckpt"
num_steps: 50000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
from_detection_checkpoint: true
}
train_input_reader {
label_map_path: "annotationslabel_map.pbtxt"
tf_record_input_reader {
input_path: "train.record"
}
}
eval_config {
num_examples: 210
max_evals: 10
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: "annotationslabel_map.pbtxt"
shuffle: false
num_epochs: 1
num_readers: 1
tf_record_input_reader {
input_path: "val.record"
}
}
列车：
python object_detection/model_main.py --logtostderr --pipeline_config_path=pipeline.config --model_dir=train
日志：
Accumulating evaluation results...
DONE (t=0.05s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
这是正常现象吗？如何解决？
我注意到的一些事情可能会帮助您：
配置文件中的num_classes为24，但您正在训练23个类。
另请注意，您正在使用固定的图像大小调整器，根据照片的尺寸，这可能是一个问题，因为您没有保持纵横比。
由于您的数据集相当小，因此通过减少步数(20k(的训练可能会略有改善。
如果这些都没有帮助，可以考虑在配置文件中添加一个硬数据挖掘器参数，以引入最少数量的反例。