Using ssd_mobilenet_v1 and v2 traning network has a low confidence loss to detect a small object and the loss can't go down.

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System information

• What is the top-level directory of the model you are using:
  work_dir: models/research/object_detection/
  model: ssd_mobilenet_v1(try also v2)

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
  I think the main is my config, I change the learning rate in many ways,however,the loss alway go down near to the 5 and stop.When I train only one class also like this.

This is my config:
`
model {
ssd {
num_classes: 6
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_v1'
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: 0
}
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: 4
optimizer {
rms_prop_optimizer: {
learning_rate: {
exponential_decay_learning_rate {
initial_learning_rate: 0.004
decay_steps: 6000
decay_factor: 0.5
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
}

fine_tune_checkpoint: "my/models-master/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/model.ckpt"
from_detection_checkpoint: true

num_steps: 500000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}

train_input_reader: {
tf_record_input_reader {
input_path: "my/models-master/research/object_detection/train_cap.record"
}
label_map_path: "my/models-master/research/object_detection/training_cap/cap.pbtxt"
}

eval_config: {
num_examples: 12538
max_evals: 10
}

eval_input_reader: {
tf_record_input_reader {
input_path: "my/models-master/research/object_detection/train_cap.record"
}
label_map_path: "my/models-master/research/object_detection/training_cap/cap.pbtxt"
shuffle: false
num_readers: 1
}
`

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):

Linux Ubuntu 16.04 Desktop Version

• TensorFlow installed from (source or binary):

Pip to install. The command line is "pip3 install tensorflow-gpu".

• TensorFlow version (use command below):
  tensorflow-gpu version: v1.14.0-rc1-22-gaf24dc91b5 1.14.0

• Bazel version (if compiling from source):
  What???

• CUDA/cuDNN version:
  CUDA 10.0
  cuDNN 7.6

• GPU model and memory:
  gtx1060 6G

• Exact command to reproduce:

python model_main.py --logtostderr --model_dir=training/ --pipeline_config_path=training_cap/ssd_mobilenet_v1_coco.config

You can collect some of this information using our environment capture script:

https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh

You can obtain the TensorFlow version with

python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"

Describe the problem

I think the main is my config, I change the learning rate in many ways,however,the loss alway go down near to the 5 and stop.When I train only one class also like this.

Source code / logs

As you see my config.