NijiFlow is a Python library wrapping TensorFlow and trained NijiNet models. Its goal is to make it super simple to run 2D-3D image classifier without any knowledge about deep learning or TensorFlow.
NijiFlow can be simply installed from PyPI with pip. You also need to install TensorFlow to make NijiFlow work.
$ pip install --user nijiflow tensorflow
Supported Python versions are 2.7 and 3.4+.
import nijiflow image_paths = [...] classifier = nijiflow.Classifier() predictions = classifier.classify(image_paths) for image_path, prediction in zip(image_paths, predictions): print('%.3f\t%s' % (prediction, image_path))
Prediction values are floating point numbers between 0 and 1. They will be >=0.5 for 2D images, <0.5 otherwise.
NijiFlow is built on top of NijiNets . NijiNets are MobileNet networks  trained for 2D-3D image classification. Details of NijiNets are described in an article in SIG2D Letters #1.
NijiFlow contains the NijiNet model based on MobileNet v1 (1.0/224), but the author also provides several trained models with different parameters. They can be downloaded from following links:
|NijiNet (1.0, 224)||12MB||99.1%||99.7%||98.5%||nijinet_1.0_224.pb|
|NijiNet (1.0, 128)||12MB||98.7%||99.8%||97.6%||nijinet_1.0_128.pb|
|NijiNet (0.25, 224)||0.9MB||98.5%||99.7%||97.4%||nijinet_0.25_224.pb|
 A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861, 2017.
 H. Tachibana. NijiFlow: MobileNets に基づくコンパクトな二次元画像判別機. SIG2D Letters #1, 2017.