This document has instructions for running ResNet50* v1.5 FP32 inference using Intel® Optimization for TensorFlow*.
Note that the ImageNet dataset is used in these ResNet50 v1.5 examples. Download and preprocess the ImageNet dataset using the instructions here. After running the conversion script you should have a directory with the ImageNet dataset in the TF records format.
Quick Start Scripts
||Runs online inference (batch_size=1).|
||Runs batch inference (batch_size=128).|
||Measures the model accuracy (batch_size=100).|
||Uses numactl to run batch inference (batch_size=128) with one instance per socket for 1500 steps and 50 warm-up steps. If no
||Uses numactl to run online inference (batch_size=1) using four cores per instance for 1500 steps and 50 warm-up steps. If no
To run on bare metal, the following prerequisites must be installed in your environment:
- Python* 3
- Intel® Optimization for TensorFlow*
Download and untar the model package and then run a quick start script.
DATASET_DIR=<path to the preprocessed imagenet dataset> OUTPUT_DIR=<directory where log files will be written> wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/resnet50v1-5-fp32-inference.tar.gz tar -xzf resnet50v1-5-fp32-inference.tar.gz cd resnet50v1-5-fp32-inference quickstart/<script name>.sh
Documentation and Sources
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Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.