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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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How are conditional models different from supervised models?

I'm wondering what the difference between conditional learning and supervised learning is - especially in diffusion models? Am I correct to assume that diffusion models are supervised because in ...
euleriwt's user avatar
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Flipping train and test labels for binary classification

I was training a GCN (this one) on a single graph (n=1,1304 nodes, num_features=26) to perform node level binary classification. However, my model performed with 5% accuracy (and even went as low to 0%...
Heisen _'s user avatar
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2 answers
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Can too much trainingdata have a negative impact?

I have to detect objects in an image. I want to use a neural network for this (yolov8). Since my objects are stacked, most of them are partially hidden and only front and side is visible. My dataset ...
Ef Ge's user avatar
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Labeling for partial hidden Objects

I want to train a ann for object detection and my first task is to label my images. These classes can be partially covered by other classes. Sometimes the middle part is not visible (left/right or top/...
Ef Ge's user avatar
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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 ...
Pigna's user avatar
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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 ...
arame3333's user avatar
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1 answer
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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. ...
tparker's user avatar
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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 ...
Alexis Winters's user avatar
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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 ...
Marques's user avatar
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1 answer
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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 ...
sangstar's user avatar
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1 answer
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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 ...
AdvilPLZ's user avatar
3 votes
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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 ...
Sanger Steel's user avatar
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14 views

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 ...
Zhang NaiChi's user avatar
7 votes
2 answers
798 views

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 ...
thzu's user avatar
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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 ...
GKozinski's user avatar
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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 ...
Tetraquark's user avatar
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31 views

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, ...
hanugm's user avatar
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1 answer
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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 ...
Harry's user avatar
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2 votes
1 answer
300 views

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 ...
NateW's user avatar
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1 answer
390 views

How to label unsupervised data for deep learning multi-classification

I have unlabeled credit card transaction data that has the following columns: ...
Sarah Grimes's user avatar
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1 answer
105 views

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 ...
Manveru's user avatar
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1 answer
219 views

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 ...
ihb's user avatar
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2 votes
0 answers
176 views

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, ...
logankilpatrick's user avatar
0 votes
1 answer
33 views

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: ...
Minions's user avatar
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4 votes
1 answer
4k views

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 ...
fnhdx's user avatar
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1 vote
0 answers
133 views

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 ...
Robin's user avatar
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1 vote
2 answers
6k views

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 ...
JJJohn's user avatar
  • 221
2 votes
1 answer
116 views

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 ...
Valery Noname's user avatar
3 votes
1 answer
132 views

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 ...
ihb's user avatar
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0 votes
0 answers
58 views

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. ...
awsuser2021's user avatar
2 votes
0 answers
35 views

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 ...
Alexander Soare's user avatar
5 votes
1 answer
104 views

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 ...
NateW's user avatar
  • 153
10 votes
1 answer
372 views

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 ...
FirePower's user avatar
  • 201
2 votes
1 answer
616 views

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 ...
Luca Hofmann's user avatar
1 vote
2 answers
623 views

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 ...
Gokulakannan's user avatar
3 votes
1 answer
91 views

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 ...
dan dan's user avatar
  • 191
2 votes
1 answer
2k views

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 ...
SmootQ's user avatar
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5 votes
2 answers
1k views

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 ...
pshlady's user avatar
  • 484