我一直在使用TensorFlow目标检测API处理自己的数据集。在训练过程中，我想知道神经网络对训练集的学习效果如何。因此，我想要在训练和评估集上分别运行评估，并在训练会话期间获取准确性（mAP）。
我的配置文件：
model {
faster_rcnn {
num_classes: 50
image_resizer {
fixed_shape_resizer {
height: 960
width: 960
}
}
number_of_stages: 3
feature_extractor {
type: 'faster_rcnn_resnet101'
first_stage_features_stride: 8
}
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: 8
width_stride: 8
}
}
first_stage_atrous_rate: 2
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.00999999977648
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.699999988079
first_stage_max_proposals: 100
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
}
}
}
conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.00999999977648
}
}
}
predict_instance_masks: true
mask_height: 33
mask_width: 33
mask_prediction_conv_depth: 0
mask_prediction_num_conv_layers: 4
}
}
second_stage_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: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
second_stage_mask_prediction_loss_weight: 4.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.003
schedule {
step: 3000
learning_rate: 0.00075
}
schedule {
step: 6000
learning_rate: 0.000300000014249
}
schedule {
step: 15000
learning_rate: 0.000075
}
schedule {
step: 18000
learning_rate: 0.0000314249
}
schedule {
step: 900000
learning_rate: 2.99999992421e-05
}
schedule {
step: 1200000
learning_rate: 3.00000010611e-06
}
}
}
momentum_optimizer_value: 0.899999976158
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "./mask_rcnn_resnet101_atrous_coco/model.ckpt"
from_detection_checkpoint: true
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
label_map_path: "./map901_label_map.pbtxt"
load_instance_masks: true
mask_type: PNG_MASKS
tf_record_input_reader {
input_path: ["./my_coco_train.record-?????-of-00005"]
}
}
eval_config: {
num_examples: 8000
max_evals: 100
num_visualizations: 25
}
eval_input_reader: {
label_map_path: "./map901_label_map.pbtxt"
shuffle: false
load_instance_masks: true
mask_type: PNG_MASKS
num_readers: 1
tf_record_input_reader {
input_path: ["./my_coco_val.record-?????-of-00001"]
}
}
我用这些参数运行了脚本。
python model_main.py --alsologtostderr \
--pipeline_config_path=${PIPELINE_CONFIG_PATH} \
--model_dir=${TRAIN_DIR} \
--num_train_steps=24000 \
--sample_1_of_n_eval_on_train_examples=25 \
--num_eval_steps=100 \
--sample_1_of_n_eval_examples=1
我认为这将运行Eval示例的评估。为了评估训练数据（检查从训练中捕获了多少特征），我已经将
--eval_training_data=True添加到参数中。我不能在运行时添加"eval_training_data"。我需要运行两个不同的训练会话。
有趣的是，添加了"eval_training_data"参数后，我得到了，
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.165
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.281
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.051
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.109
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.057
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.141
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.236
如果不使用 "eval_training_data"，会得到以下结果：
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.168
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.283
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.049
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.108
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.170
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.056
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.139
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.248
我有些困惑，我的问题如下：
- "eval_training_data"不会强制运行对象检测API对训练集进行评估吗？
- 为什么在我的情况下两个得分几乎相同，在某些情况下评估得分更好？
- 在训练会话期间需要添加哪些参数以便分别对训练集和评估集进行评估并打印出来？