Google colaboratory Tensorflow 2对象检测API低映射
我正在尝试使用Tensorflow 2.0目标检测来训练更快的r-cnn模型,但是我在0.01时得到了极低的贴图 我已经查看了Tensorboard中的培训图像,培训图像似乎没有正确加载,或者我在配置文件中出错。我正在使用Hardhat示例数据集学习RoboFlow教程。这是我的colab笔记本() 上图显示了已加载到Tensorboard中的训练数据集中使用的图像,下图为原始图像 我对这一点完全陌生,我不确定自己哪里出了问题。下面是我正在使用的配置文件Google colaboratory Tensorflow 2对象检测API低映射,google-colaboratory,tensorflow2.0,object-detection,object-detection-api,roboflow,Google Colaboratory,Tensorflow2.0,Object Detection,Object Detection Api,Roboflow,我正在尝试使用Tensorflow 2.0目标检测来训练更快的r-cnn模型,但是我在0.01时得到了极低的贴图 我已经查看了Tensorboard中的培训图像,培训图像似乎没有正确加载,或者我在配置文件中出错。我正在使用Hardhat示例数据集学习RoboFlow教程。这是我的colab笔记本() 上图显示了已加载到Tensorboard中的训练数据集中使用的图像,下图为原始图像 我对这一点完全陌生,我不确定自己哪里出了问题。下面是我正在使用的配置文件 model { faster_r
model {
faster_rcnn {
num_classes: 3
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 640
max_dimension: 640
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_resnet101_keras'
batch_norm_trainable: true
}
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
}
}
}
share_box_across_classes: true
}
}
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
use_static_shapes: true
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_static_balanced_label_sampler: true
use_matmul_gather_in_matcher: true
}
}
train_config: {
batch_size: 1
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 2000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 25000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "/content/models/research/deploy/faster_rcnn_resnet101_v1_640x640_coco17_tpu-8/checkpoint/ckpt-0"
fine_tune_checkpoint_type: "detection"
data_augmentation_options {
random_horizontal_flip {
}
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true # works only on TPUs
}
train_input_reader: {
label_map_path: "/content/train/Workers_label_map.pbtxt"
tf_record_input_reader {
input_path: "/content/train/Workers.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "/content/train/Workers_label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "/content/valid/Workers.tfrecord"
}
}
提前谢谢你 查看您的培训输出,您应该尝试以下几点之一:
num_步数
从2000增加到20000或100000。根据以前的经验,这些TF2模型往往需要相当长的时间才能收敛我已经找到了一个关于图像失真的答案,但是我仍然不确定为什么贴图非常低?