Row 62817

Row ID: 62817 | Dataset Entry | Axioma AXP Content Repository

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I believe I've implemented their data augmentation correctly, but please take a look! Here is the data transform applied to the training set and the second transform for the validation/testing set.

cifar_train_transform = transforms.Compose( [ transforms.ToTensor(), transforms.Resize(size=(32, 32), antialias=True), transforms.RandomCrop(32, pad_if_needed=True), transforms.RandomHorizontalFlip(0.5), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) cifar_test_transform = transforms.Compose( [ transforms.ToTensor(), transforms.Resize(size=(32, 32), antialias=True), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] )

Here is the snippet from the paper:

>We follow the simple data augmentation in \[24\] for training: 4 pixels are padded on each side, and a 32×32 crop is randomly sampled from the padded image or its horizontal flip. For testing, we only evaluate the single view of the original 32×32 image.

FieldValue
text I believe I've implemented their data augmentation correctly, but please take a look! Here is the data transform applied to the training set and the second transform for the validation/testing set. cifar_train_transform = transforms.Compose( [ transforms.ToTensor(), transforms.Resize(size=(32, 32), antialias=True), transforms.RandomCrop(32, pad_if_needed=True), transforms.RandomHorizontalFlip(0.5), transforms.Normalize((0.5…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
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url_encoded Z0FBQUFBQm5Lak9xX1I5bVpMRlBGT1YtRTd0alNVMTJvYWhDRUQyY1VzcF9RdkNJZVVPbTBHWTQxUTR3eWtvQVR4MV9jUmdLRTIyakF3all4TEdWVUZtMHRjVDhSV3dfdzhCM25VSDhlTXNXSmtqNDRGVHlFcV83UC1NenVQMEVjRjNQTHRlQ19WNEhKdWxtTjY4YjFyX1VoQUhxZ2lyNm85MkdIYndaQ3pTblh2SG55YW80V0QtZ3pyMEQ2NnVSazlZcEVLSHFhYUJKTGhRUTFaa3JHX2pRS2thWFpUVHlfU3B4T3BuTVktakNPaGhQOC1XVFdsUT0=

Raw Record

{
  "text": "I believe I've implemented their data augmentation correctly, but please take a look! Here is the data transform applied to the training set and the second transform for the validation/testing set.\n\n    cifar_train_transform = transforms.Compose(\n        [\n            transforms.ToTensor(),\n            transforms.Resize(size=(32, 32), antialias=True),\n            transforms.RandomCrop(32, pad_if_needed=True),\n            transforms.RandomHorizontalFlip(0.5),\n            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n        ]\n    )\n    \n    cifar_test_transform = transforms.Compose(\n        [\n            transforms.ToTensor(),\n            transforms.Resize(size=(32, 32), antialias=True),\n            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n        ]\n    )\n\nHere is the snippet from the paper:\n\n>We follow the simple data augmentation in \\[24\\] for training: 4 pixels are padded on each side, and a 32×32 crop is randomly sampled from the padded image or its horizontal flip. For testing, we only evaluate the single view of the original 32×32 image.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1hdWVSdmN3dFJCWU5WcllaaGlhYWoyOUFlZVB5NUMyUGJEcXNxR0s2ZVpLd3lGc0h2WW1mbVpmWjlqUklLN2ZPYXR6dHpYZnoxZUczRlE0ZG41SDg1WFNyTnZUQjh1SEF0eGswc21XWUNkR0E9",
  "url_encoded": "Z0FBQUFBQm5Lak9xX1I5bVpMRlBGT1YtRTd0alNVMTJvYWhDRUQyY1VzcF9RdkNJZVVPbTBHWTQxUTR3eWtvQVR4MV9jUmdLRTIyakF3all4TEdWVUZtMHRjVDhSV3dfdzhCM25VSDhlTXNXSmtqNDRGVHlFcV83UC1NenVQMEVjRjNQTHRlQ19WNEhKdWxtTjY4YjFyX1VoQUhxZ2lyNm85MkdIYndaQ3pTblh2SG55YW80V0QtZ3pyMEQ2NnVSazlZcEVLSHFhYUJKTGhRUTFaa3JHX2pRS2thWFpUVHlfU3B4T3BuTVktakNPaGhQOC1XVFdsUT0="
}
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