poor performance with ssd mobilenet v2 QAT retrain

Prerequisites

Please answer the following questions for yourself before submitting an issue.

• [ x ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
• [ y ] I am reporting the issue to the correct repository. (Model Garden official or research directory)
• [ y ] I checked to make sure that this issue has not already been filed.

1. The entire URL of the file you are using

https://github.com/tensorflow/models/tree/master/research/object_detection

2. Describe the bug

Precision and recall are low. I just have 1 class in my own dataset.
My pretrain model is ssd_mobilenet_v2_quantized_coco.
I run the training in CPU as suggested by the Coral tutorial.
I try to train a person detector in a crowded scene.

Validation matrix during trainning

3. Steps to reproduce

4. Expected behavior

Better performance

5. Additional context

pipeline config
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
feature_extractor {
type: "ssd_mobilenet_v2"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.00999999977648
}
}
activation: RELU_6
batch_norm {
decay: 0.97000002861
center: true
scale: true
epsilon: 0.0010000000475
}
}
override_base_feature_extractor_hyperparams: true
}
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
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
box_predictor {
convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.00999999977648
}
}
activation: RELU_6
batch_norm {
decay: 0.97000002861
center: true
scale: true
epsilon: 0.0010000000475
}
}
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.800000011921
kernel_size: 1
box_code_size: 4
apply_sigmoid_to_scores: false
class_prediction_bias_init: -4.59999990463
}
}
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.20000000298
max_scale: 0.949999988079
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.333299994469
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 9.99999993923e-09
iou_threshold: 0.600000023842
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.75
}
}
classification_weight: 1.0
localization_weight: 1.0
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
}
}
train_config {
batch_size: 128
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.20000000298
total_steps: 1000
warmup_learning_rate: 0.0599999986589
warmup_steps: 100
}
}
momentum_optimizer_value: 0.899999976158
}
use_moving_average: false
}
fine_tune_checkpoint: "/pretrain/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt"
from_detection_checkpoint: true
load_all_detection_checkpoint_vars: true
num_steps: 50000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
freeze_variables:
[ 'FeatureExtractor/MobilenetV2/Conv/',
'FeatureExtractor/MobilenetV2/expanded_conv/',
'FeatureExtractor/MobilenetV2/expanded_conv_1/',
'FeatureExtractor/MobilenetV2/expanded_conv_2/',
'FeatureExtractor/MobilenetV2/expanded_conv_3/',
'FeatureExtractor/MobilenetV2/expanded_conv_4/',
'FeatureExtractor/MobilenetV2/expanded_conv_5/',
'FeatureExtractor/MobilenetV2/expanded_conv_6/',
'FeatureExtractor/MobilenetV2/expanded_conv_7/']
}
train_input_reader {
label_map_path: "/config/label_map.pbtxt"
tf_record_input_reader {
input_path: "/vol/tf-records/20200727_train.records"
}
}
eval_config {
num_examples: 50
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: "/config/label_map.pbtxt"
shuffle: false
num_readers: 1
tf_record_input_reader {
input_path: "/vol/tf-records/20200727_val.records"
}
}
graph_rewriter {
quantization {
delay: 0
weight_bits: 8
activation_bits: 8
}
}

Include any logs that would be helpful to diagnose the problem.

6. System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04
• Mobile device name if the issue happens on a mobile device: Coral edge
• TensorFlow installed from (source or binary):
• TensorFlow version (use command below): 1.12
• Python version: 2.7.12
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory: