Hi Guys,
after more than one year reading, trying and study Tensorflow and also deploy an succesfully projekt.
I think its time to ask for some help. 
Iam doing some Object detection with the mobilnet_SSD_V2 model. I did allready some succesfully trainings for my custom model (detect a Pattern on the back of a person)
And i was also abel to convert everything to a egdetpu model and use it on my Raspi4 with the USB CoralTPU. As far as good. 
Now iam trying to train a better model.
For this i create a new larger Dataset and also use the nice possebilitys of Roboflow (extend the Dataset from 700pics to 1.600)
but my classification_loss looks like this:
The localizazion_loss is great but why is the classification_loss so bad?
I try to change some config in the pipeline, but does not improve 
classification_weight: 1.0 localization_weight: 0.7
Can someone help me an give me a hint?
Greetings, Rudi from Germany
some more details:
- TF1
- training in Colab
- ssd_mobilenet_v2_quantized_300x300_coco
- example pic out of TensorBoard:
Pipeline:
# Quantized trained SSD with Mobilenet v2 on MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
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: 0.7
}
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: 12
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: "/content/drive/MyDrive/Projekte/ssd_mobilnetV2_quatized_tranfarelearning/pretrained_model/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt"
fine_tune_checkpoint_type: "detection"
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the pets dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "/content/drive/MyDrive/Projekte/ssd_mobilnetV2_quatized_tranfarelearning/dataset/train.record"
}
label_map_path: "/content/drive/MyDrive/Projekte/ssd_mobilnetV2_quatized_tranfarelearning/dataset/labelmap.pbtxt"
}
eval_config: {
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}
eval_input_reader: {
tf_record_input_reader {
input_path: "/content/drive/MyDrive/Projekte/ssd_mobilnetV2_quatized_tranfarelearning/dataset/test.record"
}
label_map_path: "/content/drive/MyDrive/Projekte/ssd_mobilnetV2_quatized_tranfarelearning/dataset/labelmap.pbtxt"
shuffle: false
num_readers: 1
}
graph_rewriter {
quantization {
delay: 48000
weight_bits: 8
activation_bits: 8
}
}