Row 24183

Row ID: 24183 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 24183 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

>I’m currently doing it with a custom code with probabilities for each transformation and then applying them. Is this the way to do it?

Have a look a torchvision transforms, they will save you the time of having to write custom code > Should I instead just precompute and add the transformations thus increasing the dataset size?

Depends on why you are applying the transformations. If you’re applying them to prevent overfitting and you already have a sufficiently large dataset then I wouldn’t add them. If you doing this because your dataset is too small or you have a data imbalance in your dataset then I would just add them (probably keep them in a separate directory in case you want to remove them) >Am I correct on those assumptions?

Transformations should be random, if you just apply the same transformation to every image it defeats the purpose of applying transformations (to create more varied data)

[video on torchvision](https://youtu.be/Zvd276j9sZ8?si=kTkwtkKEKHPfNfSw)

FieldValue
text >I’m currently doing it with a custom code with probabilities for each transformation and then applying them. Is this the way to do it? Have a look a torchvision transforms, they will save you the time of having to write custom code > Should I instead just precompute and add the transformations thus increasing the dataset size? Depends on why you are applying the transformations. If you’re applying them to prevent overfitting and you already have a sufficiently large dataset then I…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-21
username_encoded Z0FBQUFBQm5Lak1DYW1OTjRrOVpsdXVwWUhXX2RxcUlQYUFMVGVHcy01SDVsNEhQRWtGM1BlUTdhRFlnNkdOcUJhNDRiVUFKdDlqVnFJLVhNSmdva0Q1ejh5R0ljeUFWLVE9PQ==
url_encoded Z0FBQUFBQm5Lak9SRXktZWdjS0VTdzZfN1I3RXpFVUhMRWxSNUkwNldDbVUySTllbnVlMWJJZFVNSWMzSFRNSGpnY3FtdHJLTVp5QkRyazE4RERoLUtIUHZhZmNNaFRtWHhUYTQydEVSVHJGNzE2S3FTbzdRaVh1M2stZFZmejlEZ2ZPejZiQW1zSXJtWk9FMU80N2diNElTeDJTcElnNHBTMW9qcHBGX1BHYUxxTTBvRGJqODh3dWtLUHI1Qkljc1lDOGxDRV92WkktcXNxcjhGbkJqNXVxM2c4TVhuUHBlQT09

Raw Record

{
  "text": ">I’m currently doing it with a custom code with probabilities for each transformation and then applying them. Is this the way to do it?    \n\nHave a look a torchvision transforms, they will save you the time of having to write custom code    \n> Should I instead just precompute and add the transformations thus increasing the dataset size?    \n\nDepends on why you are applying the transformations. If you’re applying them to prevent overfitting and you already have a sufficiently large dataset then I wouldn’t add them. If you doing this because your dataset is too small or you have a data imbalance in your dataset then I would just add them (probably keep them in a separate directory in case you want to remove them)     \n>Am I correct on those assumptions?    \n\nTransformations should be random, if you just apply the same transformation to every image it defeats the purpose of applying transformations (to create more varied data)    \n\n[video on torchvision](https://youtu.be/Zvd276j9sZ8?si=kTkwtkKEKHPfNfSw)",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5Lak1DYW1OTjRrOVpsdXVwWUhXX2RxcUlQYUFMVGVHcy01SDVsNEhQRWtGM1BlUTdhRFlnNkdOcUJhNDRiVUFKdDlqVnFJLVhNSmdva0Q1ejh5R0ljeUFWLVE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9SRXktZWdjS0VTdzZfN1I3RXpFVUhMRWxSNUkwNldDbVUySTllbnVlMWJJZFVNSWMzSFRNSGpnY3FtdHJLTVp5QkRyazE4RERoLUtIUHZhZmNNaFRtWHhUYTQydEVSVHJGNzE2S3FTbzdRaVh1M2stZFZmejlEZ2ZPejZiQW1zSXJtWk9FMU80N2diNElTeDJTcElnNHBTMW9qcHBGX1BHYUxxTTBvRGJqODh3dWtLUHI1Qkljc1lDOGxDRV92WkktcXNxcjhGbkJqNXVxM2c4TVhuUHBlQT09"
}
Explore Dataset Explore Row

Entry Information