Accuracy drop for Tensorflow object detection Post Quantization - tensorflow

Question

I am fine-tuning SSD Mobilenet v2 for a custom dataset. I am fine-tuning the model for 50k steps and quantization aware training kicks in at 48k step count.
graph_rewriter {
quantization {
delay: 48000
weight_bits: 8
activation_bits: 8
}
}
I am observing a 95%+ training, validation and testing mAP post training.
After quantization using the commands
python object_detection/export_tflite_ssd_graph.py
--pipeline_config_path=${CONFIG_FILE}
--trained_checkpoint_prefix=${CHECKPOINT_PATH}
--output_directory=${OUTPUT_DIR} --add_postprocessing_op=true
./bazel-bin/tensorflow/contrib/lite/toco/toco
--input_file=${OUTPUT_DIR}/tflite_graph.pb \
--output_file=${OUTPUT_DIR}/detect.tflite \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE \
--inference_type=QUANTIZED_UINT8 \
--input_shapes="1,300,300,3" \
--input_arrays=normalized_input_image_tensor \
--output_arrays="TFLite_Detection_PostProcess","TFLite_Detection_PostProcess:1","TFLite_Detection_PostProcess:2","TFLite_Detection_PostProcess:3" \
--std_values=128.0 --mean_values=128.0 --allow_custom_ops --default_ranges_min=0 --default_ranges_max=6
I tested the generated detect.tflite model using same test set. I see a drop in mAP to about 85%.
Is this mAP number drop to be expected? How can I improve the post quantization mAP?