I've trained a video classification model using Google's video intelligence platform, I want to now download the model to predict on-prem for security purpose but I don't see anyway of exporting the model. Is there any way to do so?
I inform you that indeed you are right. As of today the AutoML Video Intelligence is on Beta and there is no way to export your model.
I would advise you to stay alert for the Release Notes to check for updates on the product.
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I've used the Video Intelligence API to do object tracking on video.
In the document [1], it recognizes more than 20,000 objects, places, and actions in stored and streaming video.
I have a questions. Is there any document that shows what kind of objects can be recognized or can't be recognized?
It's my first question. Thank you.
[1] https://cloud.google.com/video-intelligence
In this GCP documentation, it enumerates the categories in which Cloud Video Intelligence API can detect, analyze, track, transcribe and recognize: https://cloud.google.com/video-intelligence/docs/how-to
Among the things that are listed on the GCP documentation that Cloud Video Intelligence API can detect, track and recognize are: faces, people, shot changes, explicit content, objects, logos and text. Cloud Video Intelligence API are already pre-trained, if in case there are objects that Cloud Video Intelligence API can't recognize, you can train your own custom models using AutoML Video Intelligence. To get started with AutoML Video Intelligence, you can refer to this GCP documentation: https://cloud.google.com/video-intelligence/automl/docs/beginners-guide
As to the limitation of object that can be recognized in Cloud Video Intelligence API, there is no document that states which object are not recognizable. The only limits that are in the Cloud Video Intelligence API documentation are in terms of video size, per request and length. GCP Documentation: https://cloud.google.com/video-intelligence/quotas
So I have been collecting data of numerous text-descriptions about articles, where as each description was structred differently. Now, I would have to "create" an algorithm, which sorts out the title of that article for me what is a hard task. I have come around Google ML natural language and it seems to be able to create one for me.
Unfortunately, I am not really able to exactly find out how I can use it,
so my question is... How precisely can I set it up ? And additionally, it would be helpful to know if firebase has such a service, since I am planning to build a firebase project.
Thanks in advance for any help !
Unfortunately models created using Google AutoML Natural Language are not exportable to Tensorflow lite (mobile models). Based from your use case you will need a model for text classification, the provided link has a sample of how this model work. You can follow this tutorial to train a custom model using the data that you have so it can identify if a title of a article is a hard task or not.
Once training is done you can now:
Deploy it in Firebase
Download the model in your device and perform testing.
You can find detailed instructions from training the model to testing it on your device for either iOS or android.
I am looking at Google AutoML Vision API and Google Vision API. I know that if you use Google AutoML Vision API that it is a custom model because you train ML models based on your own images and define your own labels. And when using Google Vision API, you are using a pretrained model...
However, I am wondering if it is possible to use my own algorithm (one which I created and not provided by Google) and using that instead with Vision / AutoML Vision API ? ...
Sure, you can definitely deploy your own ML algorithm on Google Cloud, without being tied up to the Vision or AutoML API.
Two approaches that I have used many times for this same use case:
Serverless approach, if your model is relatively light in terms of computational resources requirement - Deploy your own custom cloud function. More info here.
To be more specific, the way it works is that you just call your cloud function, passing your image directly (base64 or pointing to a storage location). The function then automatically allocates all required resources (automatically), run your custom algorithm to process the image and/or run inferences, send the results back and vanishes (all resources released, no more running costs). Neat :)
Google AI Platform. More info here
Use AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data.
In doubt, go for AI Platform, as the whole pipeline is nicely lined-up for any of your custom code/models. Perfect for deployment in production as well.
We are automating the process of our deep learning project. Images are automatically uploaded to a dataset in AutoML Vision (Object detection) in the Google Cloud Platform. We have a couple of team members who regularly annotate the uploaded images by using the provided Annotation Tool in Web UI. We need to measure the productivity of our team members by counting the annotations they make for each of them. I haven't found an efficient solution yet. I would appreciate it if you could share your ideas.
There is not a feature to identify who annotated which images; however, the approach I can think of is that you can split the work between your team members and distribute the labels that each one should annotate. Then you can simply count the number annotations for each label. For instance, in from this guide you can give Baked Goods and Cheese to one collaborator and Salad and Seafood to another one, and so on, so that you can check the totals in the UI. Even, the label statistics can give you more details of annotations for each label (hence for each team member), note that statistics are only available in AutoML Vision Object Detection UI.
An automated approach, in case you are interested in, is Human Labeling Service; according to documentation, currently, it is only available by email because of the Coronavirus (COVID-19) measures
If recommendations above don't fit your needs, you could always file a Feature Request for asking the desired functionality and add the required details.
I trained a model using google AutoML Vision and now I want to export it to use it locally, I tried this tutorial from Google official doc with no success.
Actually, in model list, when I click the three dots (more actions) there is no export option:
Even in the test & use page there is no option to export the model:
Thanks in advance,
First of all, the tutorial you are following is for AutoML tables and, although similar, is not exactly the same as for AutoML Vision.
For AutoML Vision you can train two types of models, Cloud hosted and Edge-exportable. As the name may infer, only the second ones can be exported.
Here you can see the documentation for exporting AutoML Vision Edge models.
My assumption is you have trained a Cloud hosted model which is not exportable.
There is currently a feature request opened to allow this behavior. You can find it here. If you would also be interested on it you can star it to keep updated about the progress.