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Image classification • MLPerf inference • TFLite CPU • Mobilenets • Linux • webcam
solution:demo-image-classification-tflite-cpu-mobilenets-linux (v1.6.0)Portable solution description
- What is the portable CK solution: docs
- Reproduced paper: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Reproduced SOTA results (crowd-benchmarking): crowd-benchmarking-mlperf-inference-classification-mobilenets
Auto-tuning results: (Pareto-efficient ML/SW/HW co-design)
- Reproducibility checklist: ACM/cTuning
- Automation framework: CK
- CK repository: A collection of all AI and ML workflows and packages in the CK format
- Benchmarking pipeline (build, run, validate): program:image-classification-tflite-codereef
- Workflow CLI and Python API (compile, run, benchmark, validate): module:program
- Workflow dependencies (model, dataset, framework): View
- Benchmarking pipeline (build, run, validate): program:image-classification-tflite-codereef
Check the prerequisites for your system
Install manually from the command line (to be automated in the future):
Sources: * https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection/optional_harness_ck/classification This solution demo was prepared by Grigori Fursin and Hervé Guillou. Requred Ubuntu packages: sudo apt update sudo apt install git wget libz-dev curl cmake sudo apt install gcc g++ autoconf autogen libtool
Install cBench (docs)
Install cBench from the command line (a small Python library to manage CK solutions):
Note that you may need to add the --user flag if you install in your user space, i.e. "python3 -m pip install cbench --user"
pip3 install cbench or python3 -m pip install cbench or pip install cbench
Init this solution with the portable workflow on your machine
Run manually from your command line
(cBench will attempt to automatically adapt this workflow to your system -
you may need to press Enter several times to select default answers for some questions):
cb init demo-image-classification-tflite-cpu-mobilenets-linux
Run this workflow locally
or start local run manually from the command line:
cb run demo-image-classification-tflite-cpu-mobilenets-linux # Note that the following CK program pipeline will be executed: ck compile program:image-classification-tflite-codereef --cmd_key=default --speed ck run program:image-classification-tflite-codereef --cmd_key=default
Successfully tested configuration
Host OS: linux-64 (Ubuntu 18.04.3 LTS)
Target OS: linux-64 (Ubuntu 18.04.3 LTS)
Target machine: LENOVO ThinkPad T470p (20J6CTO1WW)
Target CPU: Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
Target CPUs:
Target OS: linux-64 (Ubuntu 18.04.3 LTS)
Target machine: LENOVO ThinkPad T470p (20J6CTO1WW)
Target CPU: Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
Target CPUs:
- Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
- Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
- Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
- Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
Reused CK components
pip install numpy pip install opencv-python ck pull repo:ck-mlperf ck install package --tags=lib,python-package,numpy ck install package --tags=lib,python-package,cv2 ck install package:imagenet-2012-val-min ck install package:imagenet-2012-aux ck install package:lib-rtl-xopenme ck install package:dataset-imagenet-preprocessed-using-opencv ck install package --tags=lib,tflite,v1.13.1,vsrc ck install package:model-tf-mlperf-mobilenet-quantized ck compile program:image-classification-tflite-codereef --speed