This document has instructions for running DenseNet-169 FP32 inference using Intel® Optimization for TensorFlow*.
Download and preprocess the ImageNet dataset using the instructions. After running the conversion script you should have a directory with the ImageNet dataset in the TF records format.
DATASET_DIR to point to this directory when running DenseNet-169 .
Quick Start Scripts
||Runs online inference (batch_size=1).|
||Runs batch inference (batch_size=100).|
||Measures the model accuracy (batch_size=100).|
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. Set environment variables for the path to your
DATASET_DIR and an
OUTPUT_DIR where log files will be written, then run a quick start script.
DATASET_DIR=<path to the dataset> OUTPUT_DIR=<directory where log files will be written> wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/densenet169-fp32-inference.tar.gz tar -xzf densenet169-fp32-inference.tar.gz cd densenet169-fp32-inference quickstart/<script name>.sh
Documentation and Sources
Get Started Guide
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Related Containers and Solutions
DenseNet-169 FP32 Inference TensorFlow* Container