Skip to content

Latest commit

 

History

History
46 lines (33 loc) · 2.58 KB

README.md

File metadata and controls

46 lines (33 loc) · 2.58 KB

Introduction

The SqueezeNet V1.0 network can be used for image classification. The provided Makefile does the following

  1. Downloads the Caffe prototxt file and makes any changes necessary to work with the Movidius Neural Compute SDK
  2. Downloads and generates the required ilsvrc12 data
  3. Downloads the .caffemodel file which was trained and provided by BVLC.
  4. Profiles, Compiles and Checks the network using the Neural Compute SDK.
  5. There is a python example (run.py) and a C++ example (cpp/run.cpp) which both do a single inference on an image as an example of how to use this network with the Neural Compute API thats provided in the Neural Compute SDK.

Makefile

Provided Makefile describes various targets that help with the above mentioned tasks.

make help

Shows makefile possible targets and brief descriptions.

make all

Makes the following: prototxt, caffemodel, profile, compile, check, cpp, run, run_cpp.

make prototxt

Downloads the Caffe prototxt file and makes a few changes necessary to work with the Movidius Neural Compute SDK.

make caffemodel

Downloads the Caffe model file

make profile

Runs the provided network on the NCS and generates per layer statistics that are helpful for understanding the performance of the network on the Neural Compute Stick. Output diplayed on terminal and the output_report.html file is also created. Demonstrates NCSDK tool: mvNCProfile

make browse_profile

profiles the network similar to make profile and then brings up output_report.html in a browser. Demonstrates NCSDK tool: mvNCProfile

make compile

Uses the network description and the trained weights files to generate a Movidius internal 'graph' format file. This file is later loaded on the Neural Compute Stick where the inferences on the network can be executed. Demonstrates NCSDK tool: mvNCCompile

make check

Runs the network on Caffe on the CPU and compares results when run on the Neural Compute Stick. Consistency results are output to the terminal. Demonstrates the NCSDK tool: mvNCCheck.

make run_py

Runs the provided run.py python script which sends a single image to the Neural Compute Stick and receives and displays the inference results.

make cpp

Builds the C++ example program run_cpp which can be executed with make run_cpp.

make run_cpp

Runs the provided run_cpp executable program that is built via make cpp. This program sends a single image to the Neural Compute Stick and receives and displays the inference results.

make clean

Removes all the temporary and target files that are created by the Makefile.