我一直在使用TensorFlow在Linux上检测牛奶纸箱。使用的语言是python。使用的模型是fast_rcnn_inception_v2_pets。我是机器学习的新手。这是我第一次学习如何进行物体检测,请帮忙!
我们的数据集:我们已经在相同的环境(冰箱架子)中分别拍摄了每个牛奶纸箱的照片(每个牛奶纸箱约130张照片)。我们已经拍摄了多个牛奶纸盒的混合照片(约400张照片)。
以下证据表明该培训被认为是成功的:Evidence 1 Evidence 2 Evidence 3
这是我们给纸箱加标签的方式:Using OpenLabeler
这是实时摄像机供稿,它不准确(问题):Evidence
代码:
# 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: 6
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: [0.5,2.0]
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: {
exponential_decay_learning_rate {
initial_learning_rate: 0.0002
decay_steps: 5000
decay_factor: 0.9
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "/home/konbini/tensorflow1/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt"
from_detection_checkpoint: 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: 150000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "/home/tensorflow1/models/research/object_detection/train.record"
}
label_map_path: "/home/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
}
eval_config: {
num_examples: 288
# Number of images in testing folder
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
}
eval_input_reader: {
tf_record_input_reader {
input_path: "/home/tensorflow1/models/research/object_detection/test.record"
}
label_map_path: "/home/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
shuffle: false
num_readers: 1
}
当我们对任何一种颜色更改进行增强时,结果都会变得更糟。当我们对任何类型的旋转进行增强时,结果都是相同的。 Evidence
我有两个问题:我们的数据集是否有问题?我们的贴标签方法有什么问题吗?