Questions tagged [data-labelling]
For questions related to the problem of labelling data and data labelling techniques in the context of artificial intelligence.
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Is synthetic data just a placebo for immature models?
I apologize for the provocative question, but let me elaborate.
I am trying to wrap my head around the logic of synthetic data.
When you train a model what you are trying to do is to teach the ground ...
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90
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Fine Tuning a Bert Transformer. How to label for emotions and train large scripts?
From what I have seen you can fine tune a Bert model to detect emotions by labelling single sentences.
But if the text you want to evaluate is a large script with many sentences, do I need to split ...
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How can CAPTCHAs be used for both user verification and ML training?
CAPTCHAs (e.g. requiring a site visitor to click all the images of traffic lights in a grid of images) are often used throughout to Internet to verify that a site visitor is a human rather than a bot.
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What papers can I read that explore model performance vs dataset size?
I am trying to estimate how many images I need to label for an object detection task. I understand a lot of variables are at play, but I'd like to find some papers that have already explored this ...
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How to balance classes for YOLO?
The problem I am having is that to my understanding we need to annotate all objects of all classes on the images we want to train (or fine tune) our YOLO on. This is because YOLO compares labeled ...
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labelling in supervised market prediction direction problem
I'm quite frustrated about labelling methods in the context of supervised learning for market direction prediction. Let us assume that we would like to use one fancy method of AI to forecast the ...
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How do we determine what is correct and what not in Adaboost
In Adaboost, how is it determined what is correct and what not?
In the following example from StatQuest (in youtube), what correct is
and what incorrect makes sense in real life. But what if we have a ...
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Is it a good idea to have a category and its subcategories in the training set of an object segmentation model?
I am currently training an object segmentation model (detectron2 : mask rcnn)
The objective is to detect materials like wood, plastic, glass etc...
wood is one of the categories in my training set.
Is ...
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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 ...
- 21
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Semi-supervised learning algorithms creating redundant data
If I'm generating pseudo-labels that I'm confident are correct for my dataset due to high confidence scores or something else, how can I expect that the new data I'm labeling won't be redundant? To my ...
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possible to combine multiple labeled objects as one object?
So I have labeled the entire skeletal muscles in detail. For example instead of just labeling shoulders I have labeled:
Rear Delt
Middle Delt
Front Delt
but now you want all of the delts to be ...
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Using a pre-trained model to generate labels to data to then train a model on
I'm trying to set up a pipeline for my ML models to automatically re-train themselves whenever concept drift occurs to recalibrate to the new output distributions. However, I can't get ground-truth ...
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How to determine whether this situation belongs to data leakage or not
Suppose that I use three features (x1, x2, x3) to predict the value of y. After hyperparameters tuning, the r2 score on train/valid/test set is 0.92, 0.54, 0.55 respectively, it's not so good.(It is ...
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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 ...
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Is there a standard term for the following flaw in the data?
I wonder if following characteristic of data has some standard "professional" or scientific term associated with it.
Let's assume that I have a set of dog/cat images labeled 0 for a cat and ...
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What techniques exist to increase the learning importance of difficult-to-learn labels over easy ones?
I am training a model to place labels in image data. Some labels are learnt very quickly by the model while others take a long time to perfect. I cannot simply add more labeled data with only the ...
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What are the "per image" annotations that are generally used for image datasets in AI?
Computer vision is highly benefited by AI algorithms. Image data is abundantly available. There are different varieties of tasks such as image classification, prediction, segmentation, generation, ...
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356
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General approaches in text encoding and labelling for NLP [closed]
What are the approaches of encoding text data? I would be glad to hear some summarization from experienced persons.
And are there any solutions accepting words outside the vocabulary and including ...
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270
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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 ...
- 153
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338
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How to label unsupervised data for deep learning multi-classification
I have unlabeled credit card transaction data that has the following columns:
...
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97
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Should I train my network for classification on samples whose ground truth label is ambiguous?
Imagine that I am training a model to classify handwritten digits. Suppose there are some bad quality images that could be classified by a human as either 0 or 8, 1 or 7 or other commonly ...
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186
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How to handle an unbalanced dataset when training object detection algorithms?
I am training an object detection model, and I have some very highly unbalanced data annotations. I have almost 11,000 images, all with dimensions of 1024 $\times$ 1024.
Within those images I have the ...
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139
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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, ...
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An online editor that allows data labeling format [closed]
I have a set of students (~20) that will work on annotating data for an NLP project.
The annotation task will be as in the following:
...
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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 ...
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129
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How to deal with images that do not contain any object of interest?
I'm currently working on an iOS App where I want to detect if there is a table, chair or bench in the current camera input.
My idea was to take the MobileNetV2 model and get it to classify these three ...
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What is the difference between "ground truth" and "ground-truth labels"?
I'm aware that the ground-truth of the example at the top left-hand corner of the image below is "zero"
However, I am confused about the meaning of the terms ground truth and ground-truth ...
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108
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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 ...
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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 ...
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Data Augmentation of store images using handwritten labels
I am new to AI and NN. I've started learning using Geron's book on Tensorflow.
My first project ("Smart Shelf") is to determine which items in a store have been purchased and need refilled. ...
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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 ...
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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 ...
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344
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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 ...
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572
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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 ...
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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 ...
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90
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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 ...
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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 ...
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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 ...
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