2D-3D image classifier library based on MobileNets
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README.md

NijiFlow

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.

Installation

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+.

Usage

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.

NijiNet models

NijiFlow is built on top of NijiNets [2]. NijiNets are MobileNet networks [1] 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:

Network Size Accuracy Precision Recall Download
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

License

Aapache 2.0

References

[1] 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.

[2] 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.