我正在开发一个基本模型,以识别我的DGT半人马国际象棋棋盘的电子墨水显示
标识显示的边界框的大多数标签为“不适用”。在一些情况下,边界框使用“显示”标签,但这是当我在图像中识别出我身体的一部分时
我假设我的标签配置不正确,但我不确定如何调试。请参见下面的my labelmap.pbtxt以及我的培训配置
谢谢你的帮助
我的身体标有“显示”:
我的身体标有“显示器”,而电路板/显示器标有“不适用”:
标有“不适用”的显示器:
labelmap.pbtxt
item {
id: 1
name: 'display'
display_name: 'display'
}
faster_rcnn_inception_v2_pets.config
# Faster R-CNN with Inception v2, configured for Oxford-IIIT Pets Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
model {
faster_rcnn {
num_classes: 1
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_inception_v2'
first_stage_features_stride: 16
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.0002
schedule {
step: 900000
learning_rate: .00002
}
schedule {
step: 1200000
learning_rate: .000002
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt"
from_detection_checkpoint: true
load_all_detection_checkpoint_vars: true
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the pets dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/train.record"
}
label_map_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/training/labelmap.pbtxt"
}
eval_config: {
metrics_set: "coco_detection_metrics"
num_examples: 1
}
eval_input_reader: {
tf_record_input_reader {
input_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/test.record"
}
label_map_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/training/labelmap.pbtxt"
shuffle: true
num_readers: 1
}
不确定您是否已解决此问题,我的2美分-此类不适用问题出现在:
相关问题 更多 >
编程相关推荐