PSPNet-Keras-tensorflow/caffe-tensorflow/examples/imagenet/README.md

ImageNet Examples

This folder contains two examples that demonstrate how to use converted networks for image classification. Also included are sample converted models and helper scripts.

1. Image Classification

classify.py uses a GoogleNet trained on ImageNet, converted to TensorFlow, for classifying images.

The architecture used is defined in models/googlenet.py (which was auto-generated). You will need to download and convert the weights from Caffe to run the example. The download link for the corresponding weights can be found in Caffe's models/bvlc_googlenet/ folder.

You can run this example like so:

$ ./classify.py /path/to/googlenet.npy ~/pics/kitty.png ~/pics/woof.jpg

You should expect to see an output similar to this:

Image                Classified As                  Confidence
----------------------------------------------------------------------
kitty.png            Persian cat                    99.75 %
woof.jpg             Bernese mountain dog           82.02 %

2. ImageNet Validation

validate.py evaluates a converted model against the ImageNet (ILSVRC12) validation set. To run this script, you will need a copy of the ImageNet validation set. You can run it as follows:

$ ./validate.py alexnet.npy val.txt imagenet-val/ --model AlexNet

The validation results specified in the main readme were generated using this script.

Helper Scripts

In addition to the examples above, this folder includes a few additional files:

• dataset.py : helper script for loading, pre-processing, and iterating over images
• models/ : contains converted models (auto-generated)
• models/helper.py : describes how the data should be preprocessed for each model