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Automating the Evaluation of Crystallization Experiments
========================================================
This is a pretrained model described in the paper:
[Classification of crystallization outcomes using deep convolutional neural networks](https://arxiv.org/abs/1803.10342).
This model takes images of crystallization experiments as an input:
<img src="https://storage.googleapis.com/marco-168219-model/002s_C6_ImagerDefaults_9.jpg" alt="crystal sample" width="320" height="240" />
It classifies it as belonging to one of four categories: crystals, precipitate, clear, or 'others'.
The model is a variant of [Inception-v3](https://arxiv.org/abs/1512.00567) trained on data from the [MARCO](http://marco.ccr.buffalo.edu) repository.
Model
-----
The model can be downloaded from:
https://storage.googleapis.com/marco-168219-model/savedmodel.zip
Example
-------
1. Install TensorFlow and the [Google Cloud SDK](https://cloud.google.com/sdk/gcloud/).
2. Download and unzip the model:
```bash
unzip savedmodel.zip
```
3. A sample image can be downloaded from:
https://storage.googleapis.com/marco-168219-model/002s_C6_ImagerDefaults_9.jpg
Convert your image into a JSON request using:
```bash
python jpeg2json.py 002s_C6_ImagerDefaults_9.jpg > request.json
```
4. To issue a prediction, run:
```bash
gcloud ml-engine local predict --model-dir=savedmodel --json-instances=request.json
```
The request should return normalized scores for each class:
<pre>
CLASSES SCORES
[u'Crystals', u'Other', u'Precipitate', u'Clear'] [0.926338255405426, 0.026199858635663986, 0.026074528694152832, 0.021387407556176186]
</pre>
CloudML Endpoint
----------------
The model can also be accessed on [Google CloudML](https://cloud.google.com/ml-engine/) by issuing:
```bash
gcloud ml-engine predict --model marco_168219_model --json-instances request.json
```
Ask the author for access privileges to the CloudML instance.
Note
----
`002s_C6_ImagerDefaults_9.jpg` is a sample from the
[MARCO](http://marco.ccr.buffalo.edu) repository, contributed to the dataset under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
Author
------
[Vincent Vanhoucke](mailto:[email protected]) (github: vincentvanhoucke)
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