Tensorflow对象检测API中的过拟合 [英] Overfitting in Tensorflow Object detection API
问题描述
我正在自定义数据集(即车牌数据集)上训练Tensorflow对象检测API模型.我的目标是使用tensorflow lite将此模型部署到边缘设备，因此我无法使用任何RCNN系列模型.因为，我无法将任何RCNN系列对象检测模型转换为tensorflow lite模型(这是tensorflow对象检测API的限制).我正在使用
训练损失::经过15万步后，训练损失约为1.3.这似乎还可以.
评估/验证损失:经过15万步后，评估/验证损失约为3.90，这是非常高的.但是，培训和评估损失之间存在巨大差异.是否存在过度拟合?我该如何克服这个问题?我认为，培训和评估损失应该彼此接近.
- 如何减少验证/评估损失?
- 我使用的是默认配置文件，因此默认情况下
use_dropout:false.如果存在过度拟合的情况，我是否应该将其更改为use_dropout:true? - 对象检测模型的训练和验证损失的可接受范围应该是多少?
请分享您的观点.感谢您！
出现过度拟合问题的原因有很多.在神经网络中，通过查看配置文件，我想提出一些建议，以避免过度拟合./p>
use_dropout:true ，以使神经元对权重的微小变化不太敏感.
尝试在 batch_non_max_suppression 中增加 iou_threshold .
使用 l1正则化器或 l1和l2正则化器的组合.
将优化器更改为 Nadam 或 Adam 优化器.
包括更多的 Augmentation 技术.
您还可以使用 Early Stopping 来跟踪您的准确性.
或者，您可以观察 Tensorboard 图表，并在验证损失开始增加之前采取权重.
我希望尝试这些步骤可以解决模型的过拟合问题.
I am training tensorflow object detection API model on the custom dataset i.e. License plate dataset. My goal is to deploy this model to the edge device using tensorflow lite so I can't use any RCNN family model. Because, I can't convert any RCNN family object detection model to tensorflow lite model (this is the limitation from tensorflow object detection API). I am using ssd_mobilenet_v2_coco model to train the custom dataset. Following is the code snippet of my config file:
model {
ssd {
num_classes: 1
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
}
}
similarity_calculator {
iou_similarity {
}
}
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.2
max_scale: 0.95
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
}
}
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
box_predictor {
convolutional_box_predictor {
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.8
kernel_size: 1
box_code_size: 4
apply_sigmoid_to_scores: false
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
}
}
}
}
feature_extractor {
type: 'ssd_mobilenet_v2'
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid {
}
}
localization_loss {
weighted_smooth_l1 {
}
}
hard_example_miner {
num_hard_examples: 3000
iou_threshold: 0.99
loss_type: CLASSIFICATION
max_negatives_per_positive: 3
min_negatives_per_image: 3
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
batch_size: 24
optimizer {
rms_prop_optimizer: {
learning_rate: {
exponential_decay_learning_rate {
initial_learning_rate: 0.004
decay_steps: 800720
decay_factor: 0.95
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
}
fine_tune_checkpoint: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/experiments/training_SSD/ssd_mobilenet_v2_coco_2018_03_29/model.ckpt"
fine_tune_checkpoint_type: "detection"
num_steps: 150000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/training.record"
}
label_map_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/classes.pbtxt"
}
eval_config: {
num_examples: 488
num_visualizations : 488
}
eval_input_reader: {
tf_record_input_reader {
input_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/testing.record"
}
label_map_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/classes.pbtxt"
shuffle: false
num_readers: 1
}
I have total 1932 images (train images: 1444 and val images: 448). I have trained the model for 150000 steps. Following is the output from tensorboard:
DetectionBoxes Precision mAP@0.5 IOU: After 150K steps, the object detection model accuracy (mAP@0.5 IOU) is ~0.97 i.e. 97%. Which seems to be fine at the moment.
Training Loss: After 150K steps, the training loss is ~1.3. This seems to be okay.
Evaluation/Validation Loss: After 150K steps, the evaluation/validation loss is ~3.90 which is pretty high. However, there is huge difference between training and evaluation loss. Is there any overfitting exist? How can I overcome this problem? In my point of view, training and evaluation loss should be close to each other.
- How can I reduce validation/evaluation loss?
- I am using the default config file so by default
use_dropout: false. Should I change it touse_dropout: truein case overfitting exist? - What should be the acceptable range of training and validation loss for object detection model?
Please share your views. Thanking you!
There are several reasons for overfitting problem In Neural networks, by looking at your config file, I would like to suggest a few things to try to avoid overfitting.
use_dropout: true so that it makes the Neurons less sensitive to minor changes in the weights.
Try increasing iou_threshold in batch_non_max_suppression.
Use l1 regularizer or combination of l1 and l2 regularizer.
Change the optimizer to Nadam or Adam Optimizers.
Include more Augmentation techniques.
You can also use Early Stopping to track your accuracy.
Alternatively, you can observe the Tensorboard visualization, take the weights before the step where the validation loss starts increasing.
I hope trying these steps will resolve the overfitting issue of your model.
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