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1 change: 1 addition & 0 deletions .gitignore
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# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
hs_err_pid*
.DS_Store
40 changes: 40 additions & 0 deletions models/UFO_object_types/README.md
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# Instructions

In order to test this model you can download a template python script that allows you to specify models and inputs.

First clone the google code labs scripts and cd into the folder

```shell
git clone https://github.com/googlecodelabs/tensorflow-for-poets-2
cd tensorflow-for-poets-2
```

Next, while your inside the tensorflow-for-poets folder you need to run the a label image script as follows

```
python -m scripts.label_image \
--graph={LOCAITON OF THE MODEL } \
--image={LOCATION OF IMAGE TO BE CLASSIFIED} \
--label={LOCATION OF LABELS FOR CLASSIFICATION}
```

Here is a sample input and what kind of output to expect.

```Shell
python -m scripts.label_image
--graph=tf_files/object_types__retrained_graph.pb
--image=tf_files/training_data/flying_objects/90926_submitter_file3__Nic.UFOzoom2.jpg
--labels=tf_files/object_types__retrained_labels.txt


Evaluation time (1-image): 0.173s

flying objects 0.996902
spheres 0.0030978536
lights 1.9191765e-07
glowing orbs 7.516825e-11
```

If you want to automate this process for a large batch of inputs please use the temple in the above repository or use the above shell commands with output redirection.
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flying objects
glowing orbs
lights
spheres