I see in the documentation that 100 labels are allowed per dataset, but does that mean that I am restricted to only 100 labels in my model or can I re-train the same model with 100 new labels and a new dataset?
I work on automl at Google.
You are restricted to a max of 100 labels for the model. Currently, re-training the model with model data is not supported. You'll be basically adding your new data to the old data and train from scratch. If you add more labels the second round, then you'll have > 100 labels, and that's above the limit.
In your usecase, do you basically keep having new labels as you have more data?
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I want to perform classification on several different images (car, pen, table) and then use the trained weights to create filters specific to car, pen, table. And finally if I apply the car filter to an image it will give me the similarity percentage.
I am new to deep learning. I will be grateful if you guide me.
I want to build filters for each class
I want to detect whether or not an image has a specific (custom) object in it or not. I tried to go through the documentation of google cloud vertex ai, but I am confused. I am not an AI or ML engineer.
They provide the following services for image
Classification (Single Label)
Classification (Multi Label)
Image Object Detection
Image segmentation
Almost All of these features require at least two labels. At least 10 images must be assigned to each label for the features to work.
Now, suppose I have 10 cat images. One of my label name is cat. And then I will have to create another label named non_cat. right? There can be infinite possibilities of an image not having a cat. Does that mean, I upload 10 cat photos and 10 random junk photos in non_cat label??
Currently I have chosen image object detection. It detects multiple attributes of that custom object with confidence score. Should I use these score to identify the custom object in my backend application? Am I going into the right direction?
As per your explanation in comments you're right going with Object Detection model in this case.
Refer the google documentation on how to prepare the data for object detection model.
As per the documentation, the dataset can have minimum 1 label and can go maximum upto 1000 labels for an AutoML or custom-trained model.
Yes. Afer checking the accuracy of your model, you can utilize the confidence score to identify the object in your application.
I want to use Google AutoML vision API for image classification, but with an incremental learning setup - more specifically I should be able to incrementally provide new training data with possibly brand new (and previously unknown) class labels. For example, lets say I train the network today for three labels: A, B and C. Now, after a week, I want to add some new data labeled with a brand new class D. And then after another week, I want to add even newer data labeled with a brand new class E. At this point, the model should be able to classify an input image into any of those five classes, with each incremental addition to the model causing very little accuracy drop.
Is that possible with google AutoML vision API?
Currently you could keep importing new data into existing AutoML dataset and each week train a new model. There is import API and train API.
The assumption of causing very little accuracy drop may be unrealistic. There may valid cases when adding new label will make the accuracy go down. E.g. add labels that are hard to distinguish from previous labels or adding labels without performing data cleanup (adding label and not applying it to existing images in which objects with this label are visible).
This question pertains to AutoML for Video Intelligence (custom labels).
When setting up training data, you are instructed to only label videos with your custom labels in them (and not videos that don’t have that label). How does the model train to identify true negatives for custom labels?
After applying the score threshold, the predictions made by your model will fall in one of the following four categories.
We can use these categories to calculate precision and recall — metrics that help us gauge the effectiveness of our model.
I have some data like student+testpoints which i would like to plot. The test scores have a max. of 100 points, and min of 0. Like:
John 56points
Ann 72points
and so on for all the students
I have the data nicely in an array. I would like to do a barplot, with 10 bars each corresponding to a 10 point ranges of testscores, so the first bar is 0-10, etc, the last one is 90-100, and I would like the height of the bar be the number of students who have their grades in that range.
My question is, can d3 do this for me with the data format I have, or I should transform my data, do the counting, and easily plot the new data? Or can it do it without transformation? What is the proper way of showing different aspects of the same data?