Row 17904

Row ID: 17904 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 17904 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I understand what it is and how to transform images to a basic level. I want to apply several type of transforms to my dataset on pytorch.

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?

Should I instead just precompute and add the transformations thus increasing the dataset size?

Some of them have random parameters and thus would be beneficial to not precompute them, I can guess that much.

The main difference I can see is how the training changes:

- Having some fixed transformations would make the model more likely to overfit those as it is seeing the same transforms each epoch.

- Having more varied transformations and probabilities could make the model less likely to converge early and thus increasing training time.

Am I correct on those assumptions? How it is usually done? I have seen no info about this anywhere.

FieldValue
text I understand what it is and how to transform images to a basic level. I want to apply several type of transforms to my dataset on pytorch. 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? Should I instead just precompute and add the transformations thus increasing the dataset size? Some of them have random parameters and thus would be beneficial to not precompute them, I can guess that much. The main diff…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-20
username_encoded Z0FBQUFBQm5LakwtalhfS05lSHdIdVJ6SHFZQ3hCc1N6SURIV255LWVNVE1FSTFTYm9QMmpGcjVwUGZIVzhHR1VsWFR2Y3llR3Y1N0V1dllJcDZ0bERBcDJWOHd3REliS0E9PQ==
url_encoded Z0FBQUFBQm5Lak9ORXNCdTVGYkFOdFF2OGJlNlltem82WmMyX3VVUTFXY2dFMlYyMXRjcGVLVFZJQWhmcEg0TlVGbUVRUmppREN4TGVwWk5iblBVTWlidXg1bmFEaVRDc0lKQVdVbkdaVTgxa2F2aThEdXZRUHFRUUE0R1JBd0NsazFpSE1mQmFsMUpSdEtLZm11czFKYWVETDZKcFlfWk1YNEM1X1RXNExVQ1I2eVNYVUJ6NXVvUVZEaWJHaHgyMHNXTDh2NVZFNUlTMU4zcV84NUVpbm5fZ1RyX29uMlhLQT09

Raw Record

{
  "text": "\nI understand what it is and how to transform images to a basic level. I want to apply several type of transforms to my dataset on pytorch.\n\n 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\nShould I instead just precompute and add the transformations thus increasing the dataset size?\n\nSome of them have random parameters and thus would be beneficial to not precompute them, I can guess that much.\n\nThe main difference I can see is how the training changes: \n\n- Having some fixed transformations would make the model more likely to overfit those as it is seeing the same transforms each epoch.\n\n- Having more varied transformations and probabilities could make the model less likely to converge early and thus increasing training time.\n\n  \nAm I correct on those assumptions? How it is usually done? I have seen no info about this anywhere.",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-20",
  "username_encoded": "Z0FBQUFBQm5LakwtalhfS05lSHdIdVJ6SHFZQ3hCc1N6SURIV255LWVNVE1FSTFTYm9QMmpGcjVwUGZIVzhHR1VsWFR2Y3llR3Y1N0V1dllJcDZ0bERBcDJWOHd3REliS0E9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9ORXNCdTVGYkFOdFF2OGJlNlltem82WmMyX3VVUTFXY2dFMlYyMXRjcGVLVFZJQWhmcEg0TlVGbUVRUmppREN4TGVwWk5iblBVTWlidXg1bmFEaVRDc0lKQVdVbkdaVTgxa2F2aThEdXZRUHFRUUE0R1JBd0NsazFpSE1mQmFsMUpSdEtLZm11czFKYWVETDZKcFlfWk1YNEM1X1RXNExVQ1I2eVNYVUJ6NXVvUVZEaWJHaHgyMHNXTDh2NVZFNUlTMU4zcV84NUVpbm5fZ1RyX29uMlhLQT09"
}
Explore Dataset Explore Row

Entry Information