It sounds like you are looking for active learning. In active learning, the classifier learns which samples would be most useful to have labelled by a human.
There are many techniques for active learning, and many ways to adapt an existing (standard) learning algorithm to the active learning setting. The particular approach you mentioned is called "...
One can find sequence labelling libraries by searching for the term conditional random fields, the state of the art method. Probably one could also find libraries and tutorial by searching the term Named Entity Recognition, which is certainly the most standard NLP application of sequence labelling.
Here are a few libraries that I know of:
I'm not sure if it meets all your criteria (mostly because I'm not sure I understand all your criteria!), but you could have a look at ELAN:
Description: With ELAN a user can add an unlimited number of textual
annotations to audio and/or video recordings. An annotation can be a
sentence, word or gloss, a comment, translation or a description of
It's true that it's a bit of a complex process but it's worth understanding it in order to get the best out of the model.
"Feature" and "attribute" (and probably observation but I'm not 100% sure) are the same thing. The features are the ones directly used by the model (as opposed to the raw input data). For every input word a vector of ...
No. These are two separate problems. Multi-label classification and multi-class classification. In general, when we talk about classification we mean multi-class classification i.e. there are a certain number of categories and the input training samples fall into only one of these which is your case of 5 images with 1 label. In the case of 1 image with 5 ...
This is called PU Learning, and it can be used when using a probabilistic classifier and certain assumptions are met about how the data is labeled.
If the assumptions are met, you
Label positive, already labeled instances as positive
Labeled unlabeled instances as negative
Train a probabilistic classifier.
This produces the same ranking of class ...
Kaggle's NAB: a variety of sources such as AWS server metrics, Twitter volume, advertisement clicking metrics, traffic data, and more. Data is labeled.
Kaggle's Wafer: manufacturing data, 2K datapoints, 143 labeled anomalies. Measures are taken every 10 milliseconds.
If you're drawing bounding boxes around cars, you can use a pretrained model. Hundreds of pretrained networks are trained on the COCO dataset, which has a car category. Then you will get the labels as the output.
Then you can also double check with LabelImg by iterating through the folder with the keyboard arrows, and adjust the bounding boxes if they are ...
On the internet, LabelImg is pretty popular. It's a Python program that you can use to automatize drawing of bounding boxes. Alternatively there is HyperLabel, on their website they say it's free so it.s worth to give it a try.
The options available are so many I completely get lost while searching. I suggest you to read this nice review of tools first.