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42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
# ImageNet Examples
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This folder contains two examples that demonstrate how to use converted networks for
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image classification. Also included are sample converted models and helper scripts.
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## 1. Image Classification
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`classify.py` uses a GoogleNet trained on ImageNet, converted to TensorFlow, for classifying images.
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The architecture used is defined in `models/googlenet.py` (which was auto-generated). You will need
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to download and convert the weights from Caffe to run the example. The download link for the
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corresponding weights can be found in Caffe's `models/bvlc_googlenet/` folder.
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You can run this example like so:
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$ ./classify.py /path/to/googlenet.npy ~/pics/kitty.png ~/pics/woof.jpg
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You should expect to see an output similar to this:
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Image Classified As Confidence
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----------------------------------------------------------------------
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kitty.png Persian cat 99.75 %
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woof.jpg Bernese mountain dog 82.02 %
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## 2. ImageNet Validation
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`validate.py` evaluates a converted model against the ImageNet (ILSVRC12) validation set. To run
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this script, you will need a copy of the ImageNet validation set. You can run it as follows:
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$ ./validate.py alexnet.npy val.txt imagenet-val/ --model AlexNet
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The validation results specified in the main readme were generated using this script.
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## Helper Scripts
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In addition to the examples above, this folder includes a few additional files:
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- `dataset.py` : helper script for loading, pre-processing, and iterating over images
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- `models/` : contains converted models (auto-generated)
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- `models/helper.py` : describes how the data should be preprocessed for each model
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