I am trying to compare two Vehicles using AWS-Recognition, I tried various models, Is there any in-build model to compare two vehicles. Actually, we want to implement, automated Vehicle claim.
Actually, while doing some research on this, Amazon does not provide any inbuild API for this like Face-Recognition or Number Reading. We have to build our custom model.
This is called build train and deploy, We have to follow certain steps.
Rekognition custom labels feature
Collect Images
Ground Truth for images.
Labeling of datasets Manual
Enable automated data labeling
Creating Datasets
Create projects
Train models
Then it has to be an amazon notebook, and Phyton, using.
Rekognition-SDK.
However, this process is time consuming.
Related
I just finished training a Custom Azure Translate Model with a set of 10.000 sentences. I now have the options to review the result and test the data. While I already get a good result score I would like to continue training the same model with additional data sets before publishing. I cant find any information regarding this in the documentation.
The only remotely close option I can see is to duplicate the first model and add the new data sets but this would create a new model and not advance the original one.
Once the project is created, we can train with different models on different datasets. Once the dataset is uploaded and the model was trained, we cannot modify the content of the dataset or upgrade it.
https://learn.microsoft.com/en-us/azure/cognitive-services/translator/custom-translator/quickstart-build-deploy-custom-model
The above document can help you.
I would like to use Azure AutoML for forecasting where I have multiple features for one timeseries. Is there any example which I can replicate?
I have been looking into: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
and
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
but no luck using multiple features instead of only one timeseries.
Any help is greatly appreciated
It looks like you are trying to find a notebook that shows how to predict a target variable when exogenous features are provided. The OJ sample notebook you included is actually a good example to reference for this scenario.
On a second glance, you'll find that in the OJ sample, `Quantity' is a function of 'Price' and other variables. We suggest trying to focus on a single time series within the OJ dataset (a single store & brand combo) as the concept could be lost in the focus on multiple series. Also note that in this example, the OJ dataset does have multiple features, we just only specify which features need to be excluded.
OJ Sample Notebook: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
-Sabina, Azure Machine Learning PM
Please check here,
Auto-train a time-series forecast model - Azure Machine Learning | Microsoft Docs
Please check the below many models accelerator which models timeseries data (but in a different domain). This can be useful.
buswrecker/energy-many-models: An offshoot of the original AML Many-Models - for the Energy Sector (github.com)
AML AutoML forecasting models address missing data in featurization stage via forward fill if missing value is in target column or median value if in feature column. Also libraries like Prophet which are supported via Auto ML can be robust.
We use Azure Custom Vision service to detect products on store shelves. This works pretty well, but we can't understand why each subsequent iteration of training makes the forecast worse. Does the service create a model from scratch in each iteration, or does it retrain the same model?
Whether you're using the classifier or the object detector, each time you train, you create a new iteration with its own updated performance metrics.
That means that each iteration in a project is independent, built on different sets of training images.
To maintain high performance don't delete existing images from the previous iteration before retraining, because by retraining you're basically creating a new model based on the images you currently have tagged.
It is also stated in the documentation here for the classifier, and here for the object detector.
I am trying to use Azure Form Recognizer with Labeling tool to train and extract text out of images.
As per the documentation:
First, make sure all the training documents are of the same format. If you have forms in multiple formats, organize them into subfolders based on common format. When you train, you'll need to direct the API to a subfolder. (https://learn.microsoft.com/en-us/azure/cognitive-services/form-recognizer/quickstarts/label-tool#set-up-input-data)
In my case I have different formatted images. I can create different projects, label images, train them and get expected output.
Challenge in my case is, if I follow this approach I need to create different projects, train it separately and maintain several model ids.
So I just wanted to know is there any way where we can train different formats together as a single training model? Basically I want to know if we can use single model Id to extract key-value pair out of different formatted images?
This is a feature that has been asked for by a few customers. We are working on a solution for this, expecting it to arrive in a few months. For now, we suggest you to train models separately and maintain multiple model IDs.
If these are only a few different types (e.g., 2-4), and they are easily distinguishable, you can also try training them all together. For that to work, though, you'll need to label more files, and results are still likely not going to be as good as separate models.
For trying that, put approximately the same number of images for each type all in same folder, and label altogether.
If there are many different types, this is not likely to work.
So I have been playing around with Azure ML lately, and I got one dataset where I have multiple values I want to predict. All of them uses different algorithms and when I try to train multiple models within one experiment; it says the “train model can only predict one value”, and there are not enough input ports on the train-model to take in multiple values even if I was to use the same algorithm for each measure. I tried launching the column selector and making rules, but I get the same error as mentioned. How do I predict multiple values and later put the predicted columns together for the web service output so I don’t have to have multiple API’s?
What you would want to do is to train each model and save them as already trained models.
So create a new experiment, train your models and save them by right clicking on each model and they will show up in the left nav bar in the Studio. Now you are able to drag your models into the canvas and have them score predictions where you eventually make them end up in the same output as I have done in my example through the “Add columns” module. I made this example for Ronaldo (Real Madrid CF player) on how he will perform in match after training day. You can see my demo on http://ronaldoinform.azurewebsites.net
For more detailed explanation on how to save the models and train multiple values; you can check out Raymond Langaeian (MSFT) answer in the comment section on this link:
https://azure.microsoft.com/en-us/documentation/articles/machine-learning-convert-training-experiment-to-scoring-experiment/
You have to train models for each variable that you going to predict. Then add all those predicted columns together and get as a single output for the web service.
The algorithms available in ML are only capable of predicting a single variable at a time based on the inputs it's getting.