Row 7779

Row ID: 7779 | Dataset Entry | Axioma AXP Content Repository

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Forgive me as this might be a bit rambly, I just need to clear my thoughts a bit.

Say you want to categorize pictures of people into age groups, your training set could then be labelled with age cateogries.

The number of groups, how fine grained it could be, depends largely on your training data. If you had a picture of every person in the world you might be able to label them with their exact age, so the categories would be say 0 to 100 years old (in reality, maybe closer to 10 year large bins if that). In most cases you would have to reduce the number of cateogries, maybe as far as "young" and "old", to reduce the accuracy and increase the sample size as well as increase the difference between categories a bit.

Is there more examples on classification of images with numerical data acting as a 'label'? In most examples the categories seem to be a lot more discrete (say different types of flowers) than any numerical, near continous, cateogry such as age.

Would be extremely happy if someone knew some studies, or even some keywords that will point me the right direction.

Thank you.

FieldValue
text Forgive me as this might be a bit rambly, I just need to clear my thoughts a bit. Say you want to categorize pictures of people into age groups, your training set could then be labelled with age cateogries. The number of groups, how fine grained it could be, depends largely on your training data. If you had a picture of every person in the world you might be able to label them with their exact age, so the categories would be say 0 to 100 years old (in reality, maybe closer to 10 year large b…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-17
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{
  "text": "Forgive me as this might be a bit rambly, I just need to clear my thoughts a bit. \n\nSay you want to categorize pictures of people into age groups, your training set could then be labelled with age cateogries. \n\nThe number of groups, how fine grained it could be, depends largely on your training data. If you had a picture of every person in the world you might be able to label them with their exact age, so the categories would be say 0 to 100 years old (in reality, maybe closer to 10 year large bins if that). In most cases you would have to reduce the number of cateogries, maybe as far as \"young\" and \"old\", to reduce the accuracy and increase the sample size as well as increase the difference between categories a bit.\n\nIs there more examples on classification of images with numerical data acting as a 'label'? In most examples the categories seem to be a lot more discrete (say different types of flowers) than any numerical, near continous, cateogry such as age. \n\nWould be extremely happy if someone knew some studies, or even some keywords that will point me the right direction. \n\nThank you.",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-17",
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}
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