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oob_models

README.md

Step-by-Step

This document is used to list steps of reproducing Intel Optimized TensorFlow OOB models tuning zoo result.

Prerequisite

1. Installation

Recommend python 3.6 or higher version.

# Install Intel® Neural Compressor
pip install neural-compressor
pip install intel-tensorflow

Note: Supported Tensorflow Version.

2. Prepare Dataset

We use dummy data to do benchmarking with Tensorflow OOB models.

3. Prepare pre-trained model

git clone https://github.com/openvinotoolkit/open_model_zoo.git
cd open_model_zoo/tools/downloader/

./downloader.py --name ${model_name} --output_dir ${model_path}

List models names can get with open_model_zoo:

Model_name
deeplabv3
efficientnet-b0
efficientnet-b0_auto_aug
efficientnet-b5
efficientnet-b7_auto_aug
faster_rcnn_inception_v2_coco
faster_rcnn_resnet101_coco
faster_rcnn_resnet50_coco
googlenet-v1-tf
googlenet-v3
googlenet-v4-tf
i3d-rgb-tf
inception-resnet-v2-tf
license-plate-recognition-barrier-0007
resnet-50-tf
rfcn-resnet101-coco-tf
vehicle-license-plate-detection-barrier-0123
yolo-v2-tf
yolo-v3-tf
  • Download with URL
Model name URL
faster_rcnn_resnet101_ava_v2.1 http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_ava_v2.1_2018_04_30.tar.gz
faster_rcnn_resnet101_kitti http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_kitti_2018_01_28.tar.gz
faster_rcnn_resnet101_lowproposals_coco http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_lowproposals_coco_2018_01_28.tar.gz
image-retrieval-0001 https://download.01.org/opencv/openvino_training_extensions/models/image_retrieval/image-retrieval-0001.tar.gz
SSD ResNet50 V1 FPN 640x640 (RetinaNet50) http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz
ssd_inception_v2_coco http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2018_01_28.tar.gz
ssd-resnet34 300x300 https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/ssd_resnet34_fp32_bs1_pretrained_model.pb

Run

run tuning

./run_tuning.sh --topology=${model_topology} --dataset_location= --input_model=${model_path} --output_model=${output_model_path}

run benchmarking

./run_benchmark.sh --topology=${model_topology} --dataset_location= --input_model=${model_path} --mode=benchmark --batch_size=1 --iters=200