我一直在使用Tensorflow Object Detection API在我自己的数据集上。在训练时，我想知道神经网络从训练集中学习的情况如何。因此，我想对训练集和评估集进行评估，并在训练期间分别获得准确性 (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
我认为这将对 Eval 示例进行评估。评估训练数据(检查从训练中捕获了多少特征)我添加了 --eval_training_data=True 参数。
我无法随时添加“eval_training_data”。我需要进行 2 次不同的训练。
有趣的是，我添加了“eval_training_data”参数，
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
虽然没有“eval_training_data”我得到了
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
我只是糊涂了。我的问题是:
- “eval_training_data”是否强制对象检测 API 在训练集上运行评估？
- 为什么在我的情况下两个分数几乎相同，在某些情况下 eval 分数更好？
- 需要添加哪些参数才能在训练期间分别评估训练集和评估集并打印？
请您参考如下方法:
通过快速查看 repo 协议(protocol)，我可以收集到的信息是:
eval_training_data 仅对训练集进行评估，并将评估集排除在评估过程之外。所以它只在你的训练集上运行。
分数相同不是坏事。这实际上很好，表明您的模型没有过度拟合，如果训练数据分数的评估明显高于评估数据的评估分数，就会出现这种情况。在某些情况下，评估得分较高是因为它必须是一个小得多的数据集，因此分数可能会随着预测的好坏情况而变化更大。 此外，该模型正在学习特征并将其与类相关联，而不是学习示例，因此不要指望它在训练集上表现出色，因为它已经看到了所有这些。 您的模型在验证集上的表现越好意味着它的泛化效果越好。
如果您设置 eval_training_data = True，它实际上已经在单独评估训练集，如果它设置为 false(默认情况下)，它只会单独评估 eval 集。 我不确定他们是否添加了用于同时评估两者的功能，但您可以通过对 model_main.py 进行非常小的更改来完成。只需添加此内容即可。它不是干净和优化的，但我猜你明白了这一点并可以相应地修改它。
flags.DEFINE_boolean('eval_training_data_and_eval_data', False,
'This will evaluate botht the training data and evaluation data sequentially')
if FLAGS.checkpoint_dir:
if FLAGS.eval_training_data_and_eval_data:
name = 'training_data'
input_fn = eval_on_train_input_fn
if FLAGS.run_once:
estimator.evaluate(input_fn,
steps=None,
checkpoint_path=tf.train.latest_checkpoint(
FLAGS.checkpoint_dir))
else:
model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
train_steps, name)
name = 'validation_data'
# The first eval input will be evaluated.
input_fn = eval_input_fns[0]
if FLAGS.run_once:
estimator.evaluate(input_fn,
steps=None,
checkpoint_path=tf.train.latest_checkpoint(
FLAGS.checkpoint_dir))
else:
model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
train_steps, name)
else:
train_spec, eval_specs = model_lib.create_train_and_eval_specs(
train_input_fn,
eval_input_fns,
eval_on_train_input_fn,
predict_input_fn,
train_steps,
eval_on_train_data=False)
# Currently only a single Eval Spec is allowed.
tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
此外，请确保您也为数据集提供了正确且不同的路径。请注意，如果我们根据验证分数优化超参数，则验证分数是有偏差的，不再是对泛化的良好估计。为了正确估计泛化，我们必须计算另一个测试集上的分数。
