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:


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:

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:

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)