• Automation framework: CK
  
• Development repository: ck-tensorflow-codereef
  
• Source: GitHub
  
• Available command lines:
  
  • ck run program:image-classification-tflite-codereef-android --cmd_key=default (META)
    
• Support for host OS: any
  
• Support for target OS: android, linux
  
• Tags: image-classification,tflite,lang-cpp,codereef-workflow,codereef-workflow-webcam
  
• Template: Image Classification via TFLite (C++)
  
• How to get the stable version via the client:
  
  pip install cbench
  cb download program:image-classification-tflite-codereef-android --version=1.0.0 --all
  ck run program:image-classification-tflite-codereef-android
• How to get the development version:
  
  pip install ck
  ck pull repo --url=https://github.com/code-reef/ck-tensorflow-codereef
  ck run program:image-classification-tflite-codereef-android
  

TensorFlow Lite image classification program

This program uses a statically linked TensorFlow Lite library.

Prerequisites

SciPy

# apt install liblapack-dev libatlas-dev
# python -m pip install scipy

TensorFlow library

$ ck install package:lib-tflite-0.1.7-src-static [--target_os=android23-arm64]

NB: Use --target_os=android23-arm64 to build for Android API 23 (v6.0 "Marshmallow") or similar.

TensorFlow models

Install a model providing a graph as tflite file e.g.:

$ ck install package:tensorflowmodel-mobilenet-v1-1.0-224-2018_02_22-py
$ ck install package:tensorflowmodel-mobilenet-v2-1.0-224-py

ImageNet dataset

$ ck install package --tags=small_dataset,imagenet,raw,val
$ ck install package:imagenet-2012-aux 

Build

$ ck compile program:image-classification-tflite [--target_os=android23-arm64]

Run

$ ck run program:image-classification-tflite [--target_os=android23-arm64]

Program parameters

Input image parameters

CK_IMAGE_FILE

If set, the program will classify a single image instead of iterating over a dataset. When only the name of an image is specified, it is assumed that the image is in the ImageNet dataset.

$ ck run program:image-classification-tflite --env.CK_IMAGE_FILE=/tmp/images/path-to-image.jpg
$ ck run program:image-classification-tflite --env.CK_IMAGE_FILE=ILSVRC2012_val_00000011.JPEG

CK_RECREATE_CACHE

If set to YES, then all previously cached images will be erased.

Default: NO.

Input preprocessing parameters

CK_TMP_IMAGE_SIZE

The size of an intermediate image. If this preprocessing parameter is set to a value greater than the input image size defined by the model, input images will be first scaled to this size and then cropped to the input size.

For example, if --env.CK_TMP_IMAGE_SIZE=256 is specified for MobileNets models with the input image resolution of 224, then input images will be first resized to 256x256 and then cropped to 224x224.

Default: 0.

CK_CROP_PERCENT

The percentage of the central image region to crop. If this preprocessing parameter is set to a value between 0 and 100, then loaded images will be cropped according this percentage and then scaled to the input image size defined by the model.

Default: 87.5.

NB: If CK_TMP_IMAGE_SIZE is set and valid, this parameter is not used.

CK_SUBTRACT_MEAN

If set to YES, then the mean value will be subtracted from the input image.

Default: YES.

• 1.0.0 (2019-12-31)

• .cm/desc.json (3 bytes)
• .cm/info.json (330 bytes)
• .cm/meta.json (3212 bytes)
• README.md (2632 bytes)
• benchmark.h (17838 bytes)
• classification.cpp (5700 bytes)
• codereef_preprocess.py (655 bytes)