Image Recognition

Inception-v3 is trained for the ImageNet Large Visual Recognition Challenge using the data from 2012. This is a standard task in computer vision, where models try to classify entire images into 1000 classes, like "Zebra", "Dalmatian", and "Dishwasher"

This tutorial will teach you how to use Inception-v3. You'll learn how to classify images into 1000 classes in Python or C++. We'll also discuss how to extract higher level features from this model which may be reused for other vision tasks.

Usage with Python API

classify_image.py downloads the trained model from tensorflow.org when the program is run for the first time. You'll need about 200M of free space available on your hard disk.

Start by cloning the TensorFlow models repo from GitHub. Run the following commands:

cd models/tutorials/image/imagenet
python classify_image.py

The above command will classify a supplied image of a panda bear.

参考文档：

[1] https://www.tensorflow.org/tutorials/image_recognition

[2] https://blog.csdn.net/muyiyushan/article/details/64124953

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