Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [data-labelling]

For questions related to the problem of labelling data and data labelling techniques in the context of artificial intelligence.

38 questions
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How to deal with a small amount of labeled samples?

I'm trying to develop skills to deal with very small amounts of labeled samples (250 labeled/20000 total, 200 features) by practicing on Kaggle "Don't Overfit" dataset (Traget_Practice have provided all 20,000 Targets). I've read a ton of papers and…
FirePower
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Is there an argument against using the (reviewed) predictions of a model as ground truth to further train exactly this model?

I plan to use my predictions as ground truth to continue training my model. These predictions are of course reviewed during this process. Is there an argument against that (reinforcement of slight mistakes/overfitting etc.)? Here my specific use…
thzu
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What is the effect of mislabeled training data?

Collecting and labeling training data for supervised learning tasks is incredibly time-consuming and costly. For instance, let's say you wrote a script that went on Google images and got you 5000 pictures for each of 10 classes. You then use an…
pshlady
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Do models train better if the labelling information is more specific (or dense)?

I'm working on a project where there is a limited dataset of videos (about 200). We want to train a model that can detect a single class in the videos. That class can be of multiple different types of shapes (thin wire, a huge area of the screen,…
NateW
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4
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How to add negative samples for object detection?

My question is: how to add certain negative samples to the training dataset to suppress those samples that are recognized as the object. For example, if I want to train a car detector. All my training images are outdoor images with at least one car.…
fnhdx
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How can computers beat humans at image recognition, if humans may incorrectly label the images?

For supervised learning, humans have to label the images computers use to train in the first place, so the computers will probably get wrong the images that humans get wrong. If so can computers beat humans?
dan dan
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Is there a methodology for splitting up annotated orthophotos into smaller photos that retain the original bounding boxes?

I'm trying to train an object detection algorithm (i.e. YOLOv4 Scaled, Faster R-CNN) on data taken from large orthophotos. Let's say I have one class, and I label the entire orthophoto with bounding boxes. After labeling, is there a way to slice up…
ihb
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How to label edited images after data augmentation?

I am new to neural networks, I've only started studying and learning about the subject a year ago, and I just started building my first neural network. The project is a little bit ambitious: A browser extension for children's safety, it checks for…
SmootQ
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How to decide which column has more weightage to output

As per Image we can see Column_A value is directly proportional to output, While Change in value of Column_B has no effects in output. So basically I want to know is there any algorithm where I can get weightage of columns which is affecting more…
Marques
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Best practice for handling letterboxed images for non fully-convolutional deep learning networks?

I'm working on a depth estimation network. It has two outputs: A relative depth map A scalar for scaling the relative depth map into an absolute depth map. This second output uses dense layers so we cannot use variable-sized input. We are trying…
NateW
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Is soft labeling the same thing as label smoothing?

I have some data with soft labels and I am trying to figure out the best approach to solve the problem with Machine Learning (since regular classification is of the table, i.e. hard labels). However, whenever I look up "soft label" materials, I keep…
logankilpatrick
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Is intersection of labels acceptable in computer vision?

I have a dataset, where objects are very close to each other. So, the question is: what is the best approach to label them? There are two possible options: mark objects so that they will not intersect (it is difficult, surroundings are not included…
Valery Noname
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For binary classification learning problems, how should I label instances where I'm only 60% sure?

I've come across a few binary classification problems lately where the labelling was challenging even for an expert. I'm wondering what I should do with this. Here are some of my suggestions to get the ball rolling: Make a third category called…
Alexander Soare
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How to detect multiple playing cards of the same class with a neural network?

I want to train an AI to detect the class (i.e. suit and rank) of playing cards. Playing cards from different decks may use slightly different shapes or colors to represent these attributes, and I want the system to work across many decks. I bought…
Luca Hofmann
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How do I change the annotations of variable-size images after having resized the images to a fixed size?

In the data-sets like coco-text and total-text, the images are of different sizes (height*width). I'm using these data sets for text detection. I want to create a DNN model for this. So the input data should be of same size. If I resize these images…
Gokulakannan
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