3

I think the crucial point here is what you precisely mean by mislabelled. Google's image classifier will likely do a 'pretty good' job of retrieving images with the given subject included, but how strict or lenient your class requisites are is quite important. For example, if one of your classes is 'dog' there may be hundreds of images procured from scraping ...


2

Accuracy can sometimes be a very coarse metric. When it is applied to three class problems, people often take the class label with maximum predicted probability and predict that. The probabilities of the individual labels are ignored. I'd recommend that as well as accuracy you calculate sensitivity and specificity for each class and the area under the ROC ...


2

To know if your model needs more training data, try to plot out "learning curves", that are based on increasing size of the training set. Basically, you calculate training and validation accuracy metrics for 1, 2, 3, 4, 5, ..., m training samples. Size of validation set may be constant over time. If the accuracy is still rising when your data set is fully ...


1

In my opinion, the second option will be more general. You can refer to some famous datasets for object detection task such as COCO or Pascal VOC, they usually accept the intersect annotations. As the image below, image from this link where they process the annotation of COCO dataset. I think the reason is that the model will be easier to separate the ...


1

I will break it down for you in very simple words. The accuracy will drop down as you label them wrong. In simpler words- accuracy is directly proportional on how perfect the data is labelled. If you think about it, suppose you have 2 categories-cats and dogs, and you have a dataset of 10,000 pictures. Out of which 50 are wrongly labelled. The accuracy will ...


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