Hi Igusm,
thank you for the answer. Let me explain our issue again.
We had dataset with our images. Each image was labelled with 4 classes. From this dataset we created our model with help of google colab:
train_data=object_detector.DataLoader.from_pascal_voc('...','...'['Truck','Car','Wheel','Jet'])
validation_data =...
model=object_detector.create(train_data,model_spec=spec,epochs=80,batch_size=8,train_whole_model=True, validation_data=validation_data)
Our accuracy aftet the quantization

{‘AP’: 0.57462365,
‘AP50’: 0.69849122,
‘AP75’: 0.58790124,
‘AP_/Truck’: 0.7065886,
‘AP_/Car’: 0.5276099,
‘AP_/Jet’: 0.70565295,
‘AP_/Wheel’: 0.49516096,

After this we created new model, from the same images but each image was re-labeled (improvement for bounding boxes) + added 4 new classes.
Now from the new model we are getting these results after the quantization:

{‘AP’: 0.37384665,
‘AP50’: 0.49949104,
‘AP75’: 0.39470323,
‘AP_/Truck’: 0.6365886,
‘AP_/BVP’: 0.16584158,
‘AP_/Pilon’: 0.035643563,
‘AP_/Ship’: 0.27283004,
‘AP_/Scooter’: 0.21544555,
‘AP_/Car’: 0.50354499,
‘AP_/Jet’: 0.61459885,
‘AP_/Wheel’: 0.36116096,

And we are able to detect always only 4 previous classes (Car,Jet,Truck,Wheel), even if we set our threshold to 0.01f. We tried to detect all classes also with python script with random image, but the results are the same.
Edit: Updated accuracy.
Thanks for any advices!