Model Zoo for AI Model Efficiency Toolkit

We provide a collection of popular neural network models and compare their floating point and quantized performance. Results demonstrate that quantized models can provide good accuracy, comparable to floating point models. Together with results, we also provide recipes for users to quantize floating-point models using the AI Model Efficiency ToolKit (AIMET).

Table of Contents

• Introduction
• Tensorflow Models
  • Model Zoo
  • Detailed Results
• PyTorch Models
  • Model Zoo
  • Detailed Results
• Examples
• Team
• License

Introduction

Quantized inference is significantly faster than floating-point inference, and enables models to run in a power-efficient manner on mobile and edge devices. We use AIMET, a library that includes state-of-the-art techniques for quantization, to quantize various models available in TensorFlow and PyTorch frameworks. The list of models is provided in the sections below.

An original FP32 source model is quantized either using post-training quantization (PTQ) or Quantization-Aware-Training (QAT) technique available in AIMET. Example scripts for evaluation are provided for each model. When PTQ is needed, the evaluation script performs PTQ before evaluation. Wherever QAT is used, the fine-tuned model checkpoint is also provided.

Tensorflow Models

Model Zoo

Network	Model Source [1]	Floating Pt (FP32) Model [2]	Quantized Model [3]	Results [4]	Documentation
ResNet-50 (v1)	GitHub Repo	Pretrained Model	See Documentation	(ImageNet) Top-1 Accuracy FP32: 75.21% INT8: 74.96%	ResNet50.md
MobileNet-v2-1.4	GitHub Repo	Pretrained Model	Quantized Model	(ImageNet) Top-1 Accuracy FP32: 75% INT8: 74.21%	MobileNetV2.md
EfficientNet Lite	GitHub Repo	Pretrained Model	Quantized Model	(ImageNet) Top-1 Accuracy FP32: 74.93% INT8: 74.99%	EfficientNetLite.md
SSD MobileNet-v2	GitHub Repo	Pretrained Model	See Example	(COCO) Mean Avg. Precision (mAP) FP32: 0.2469 INT8: 0.2456	SSDMobileNetV2.md
RetinaNet	GitHub Repo	Pretrained Model	See Example	(COCO) mAP FP32: 0.35 INT8: 0.349 Detailed Results	RetinaNet.md
Pose Estimation	Based on Ref.	Based on Ref.	Quantized Model	(COCO) mAP FP32: 0.383 INT8: 0.379, Mean Avg.Recall (mAR) FP32: 0.452 INT8: 0.446	PoseEstimation.md
SRGAN	GitHub Repo	Pretrained Model	See Example	(BSD100) PSNR/SSIM FP32: 25.45/0.668 INT8: 24.78/0.628 INT8W/INT16Act.: 25.41/0.666 Detailed Results	SRGAN.md

^[1] Original FP32 model source
^[2] FP32 model checkpoint
^[3] Quantized Model: For models quantized with post-training technique, refers to FP32 model which can then be quantized using AIMET. For models optimized with QAT, refers to model checkpoint with fine-tuned weights. 8-bit weights and activations are typically used. For some models, 8-bit weights and 16-bit activations (INT8W/INT16Act.) are used to further improve performance of post-training quantization.
^[4] Results comparing float and quantized performance
^[5] Script for quantized evaluation using the model referenced in “Quantized Model” column

Detailed Results

RetinaNet

(COCO dataset)

Average Precision/Recall	@[ IoU | area | maxDets]	FP32	INT8
Average Precision	@[ 0.50:0.95 | all | 100 ]	0.350	0.349
Average Precision	@[ 0.50 | all | 100 ]	0.537	0.536
Average Precision	@[ 0.75 | all | 100 ]	0.374	0.372
Average Precision	@[ 0.50:0.95 | small | 100 ]	0.191	0.187
Average Precision	@[ 0.50:0.95 | medium | 100 ]	0.383	0.381
Average Precision	@[ 0.50:0.95 | large | 100 ]	0.472	0.472
Average Recall	@[ 0.50:0.95 | all | 1 ]	0.306	0.305
Average Recall	@[0.50:0.95 | all | 10 ]	0.491	0.490
Average Recall	@[ 0.50:0.95 | all |100 ]	0.533	0.532
Average Recall	@[ 0.50:0.95 | small | 100 ]	0.345	0.341
Average Recall	@[ 0.50:0.95 | medium | 100 ]	0.577	0.577
Average Recall	@[ 0.50:0.95 | large | 100 ]	0.681	0.679

SRGAN

Model	Dataset	PSNR	SSIM
FP32	Set5/Set14/BSD100	29.17/26.17/25.45	0.853/0.719/0.668
INT8/ACT8	Set5/Set14/BSD100	28.31/25.55/24.78	0.821/0.684/0.628
INT8/ACT16	Set5/Set14/BSD100	29.12/26.15/25.41	0.851/0.719/0.666

PyTorch Models

Model Zoo

Network	Model Source [1]	Floating Pt (FP32) Model [2]	Quantized Model [3]	Results [4]	Documentation
MobileNetV2	GitHub Repo	Pretrained Model	Quantized Model	(ImageNet) Top-1 Accuracy FP32: 71.67% INT8: 71.14%	MobileNetV2.md
EfficientNet-lite0	GitHub Repo	Pretrained Model	Quantized Model	(ImageNet) Top-1 Accuracy FP32: 75.42% INT8: 74.44%	EfficientNet-lite0.md
DeepLabV3+	GitHub Repo	Pretrained Model	Quantized Model	(PascalVOC) mIOU FP32: 72.62% INT8: 72.22%	DeepLabV3.md
MobileNetV2-SSD-Lite	GitHub Repo	Pretrained Model	Quantized Model	(PascalVOC) mAP FP32: 68.7% INT8: 68.6%	MobileNetV2-SSD-lite.md
Pose Estimation	Based on Ref.	Based on Ref.	Quantized Model	(COCO) mAP FP32: 0.364 INT8: 0.359 mAR FP32: 0.436 INT8: 0.432	PoseEstimation.md
SRGAN	GitHub Repo	Pretrained Model (older version from here)	See Example	(BSD100) PSNR/SSIM FP32: 25.51/0.653 INT8: 25.5/0.648 Detailed Results	SRGAN.md
DeepSpeech2	GitHub Repo	Pretrained Model	See Example	(Librispeech Test Clean) WER FP32 9.92% INT8: 10.22%	DeepSpeech2.md

^[1] Original FP32 model source
^[2] FP32 model checkpoint
^[3] Quantized Model: For models quantized with post-training technique, refers to FP32 model which can then be quantized using AIMET. For models optimized with QAT, refers to model checkpoint with fine-tuned weights. 8-bit weights and activations are typically used. For some models, 8-bit weights and 16-bit weights are used to further improve performance of post-training quantization.
^[4] Results comparing float and quantized performance
^[5] Script for quantized evaluation using the model referenced in “Quantized Model” column

Detailed Results

SRGAN Pytorch

Model	Dataset	PSNR	SSIM
FP32	Set5/Set14/BSD100	29.93/26.58/25.51	0.851/0.709/0.653
INT8	Set5/Set14/BSD100	29.86/26.59/25.55	0.845/0.705/0.648

Examples

Install AIMET

Before you can run the example script for a specific model, you need to install the AI Model Efficiency ToolKit (AIMET) software. Please see this Getting Started page for an overview. Then install AIMET and its dependencies using these Installation instructions.

NOTE: To obtain the exact version of AIMET software that was used to test this model zoo, please install release 1.13.0 when following the above instructions.

Running the scripts

Download the necessary datasets and code required to run the example for the model of interest. The examples run quantized evaluation and if necessary apply AIMET techniques to improve quantized model performance. They generate the final accuracy results noted in the table above. Refer to the Docs for TensorFlow or PyTorch folder to access the documentation and procedures for a specific model.

Team

AIMET Model Zoo is a project maintained by Qualcomm Innovation Center, Inc.

License

Please see the LICENSE file for details.