SSD MobileNet V2 TFLite model without quantization output is correct, but Post training quantized model inference output is wrong

Prerequisites

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

• I am using the latest TensorFlow Model Garden release and TensorFlow 2.
• I am reporting the issue to the correct repository. (Model Garden official or research directory)
• 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

I have trained a custom object detection model using object detection api using pretrained model ssd_mobilenet_v2_320x320_coco17_tpu-8. The trained model has been exported to TFLite-friendly intermediate SavedModel which is used by TFLiteConverter to convert into TFLite format. The converted TFLite model inference is correct. But if i apply post training quantization, the inference output is wrong. Download my repository and run the jupyter notebook to explain the issue im facing.

3. Steps to reproduce

Run the jupyter notebook in the repository to reproduce the issue. Check the non-quantized model output and quantized model output.

4. Expected behavior

The inference result of quantized model should be correct.

5. Additional context

Non-quantized model output:
tflite_detection_scores: [0.88126606 0.8636228 0.8444784 0.83444893 0.82267493 0.7586309
0.72747076 0.64393556 0.4792308 0.37496698 0.35456866 0.21228562
0.17112313 0.16550326 0.16282031 0.13535884 0.12606364 0.12315518
0.09494544 0.08832499]
tflite_detection_classes: [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
tflite_num_detections: 20
tflite_detection_boxes:
[0.13898835 0.5581504 0.2226857 0.64273024]
[0.37905622 0.51477677 0.57565093 0.6674817 ]
[0.10336263 0.36700267 0.16064209 0.4233514 ]
[0.17918263 0.47067225 0.25840816 0.5481311 ]
[0.19362032 0.26374823 0.28584093 0.33670342]
[0.19328472 0.03578638 0.2740516 0.08890636]
[0.38459224 0.16602626 0.57033443 0.27695608]
[0.08592343 0.43386286 0.14284348 0.48653913]
[0.0707082 0.43492046 0.12961109 0.48729852]
[0.07322308 0.05297361 0.12850046 0.09446552]
[0.05345578 0.34245595 0.10406741 0.3869144 ]
[0.19365428 0.0161516 0.26964656 0.04439866]
[0.03531459 0.19652161 0.08658443 0.23899463]
[0.05851648 0.00280525 0.116818 0.0176958 ]
[0.01804176 0.2531051 0.06837644 0.2983545 ]
[0.01627679 0.0013474 0.06487057 0.03884105]
[0.02952993 0.17788893 0.0826961 0.22692427]
[ 0.08686746 -0.00028265 0.1465877 0.00760585]
[0.02422631 0.22625658 0.07527018 0.2654607 ]
[0.94961053 0.0057383 0.99573547 0.02748624]

Quantized model output:
tflite_detection_scores: [0.6016884 0.33555698 0.248775 0.22563314 0.21116948 0.18802762
0.17645669 0.15331483 0.1417439 0.13017297 0.1243875 0.11860204
0.11860204 0.11281657 0.11281657 0.11281657 0.10992384 0.10992384
0.10992384 0.10992384]
tflite_detection_classes: [2 2 2 2 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3]
tflite_num_detections: 20
tflite_detection_boxes:
[-0.00831555 0.06652438 1.0061812 1.0061812 ]
[-0.01247332 0.25778198 0.65277046 0.9812346 ]
[0.07068215 0.4615129 1.0477589 0.9978657 ]
[ 0.41993514 -0.00831555 0.9978657 1.0061812 ]
[0.43240845 0.84818584 0.6694016 0.9978657 ]
[0.6361394 0.9271835 1.0477589 0.9895501]
[0.18709981 0.9354991 0.59040385 0.9937079 ]
[0.1579954 0.87313247 0.49893284 1.0020235 ]
[0.661086 0.8689747 0.9853924 1.0020235]
[0.00831555 0.9521302 0.04573551 0.9937079 ]
[-0.00415777 0.8565014 0.21204646 1.0020235 ]
[0.29104415 0.8648169 0.5654572 1.0020235 ]
[0.3326219 0.38667294 0.9479724 0.9770768 ]
[0.01247332 0.00415777 0.05405106 0.16215317]
[0.33677965 0.9271835 0.6652438 0.9937079 ]
[0.03326219 0.00831555 1.0020235 0.4116196 ]
[0.00831555 0.8648169 0.04989328 1.0020235 ]
[0. 0.9313413 0.42409292 0.9895501 ]
[0.29520193 0.57793057 0.49893284 0.8980791 ]
[0.61950827 0.00415777 1.0311279 0.06652438]

6. System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
• Mobile device name if the issue happens on a mobile device:
• TensorFlow installed from (source or binary): Binary
• TensorFlow version (use command below): v2.8.0-rc1-32-g3f878cff5b6 2.8.0
• Python version: Python 3.9.9
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version: 11.5 / 8.1
• GPU model and memory: NVIDIA Geforce RTX 3060 Ti / 8 GB