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torch.nn.ResNet.2a12408ac25b016a (1)
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torch.nn.MaxPool2d.5da50241a77ad583 (1)
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torch.nn.ReLU.efe920513d89362 (1)
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torch.nn.Sequential.b3ec5028619ec2d7 (1)
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torch.nn.Conv2d.37391f7ea91bd2b8 (1)
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torch.nn.BatchNorm2d.bf5118249736c286 (1)
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torch.nn.ReLU.785475c44527bddc (1)
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torch.nn.BasicBlock.c305911c39c11a41 (1)
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torch.nn.BatchNorm2d.fe6f9a892ae33786 (1)
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torch.nn.BatchNorm2d.3643ac7a4cf3474e (1)
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torch.nn.Conv2d.1fb76503fcc525fe (1)
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torch.nn.Conv2d.f1ffcf0a3485e686 (1)
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torch.nn.ReLU.88ba06b33cf1de90 (1)
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Supervised Classification on tiniest-imagenet-200
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uploader_id : 42266 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label
tiniest-imagenet-200 (1)
Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64 x 64 colored images. !!! This dataset only links to 20 images per class (instead of the usual 500) and is ONLY…
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4000 instances - 2 features - 200 classes - 0 missing values
tiny-imagenet-200 (4)
Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64 x 64 colored images. Each class has 500 training images, 50 validation images, and 50 test images. The dataset…
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100000 instances - 3 features - 200 classes - 0 missing values
odds (1)
odds wins losses
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297 instances - 9 features - 4 classes - 1790 missing values
IRT Diverse Dataset Benchmark
Dataset Benchmark generated with Item-Response Theory (IRT): diverse collection with 30 datasets
24 datasets, 24 tasks, 0 flows, 0 runs
torch.nn.BasicBlock.f718b95c9cd4cb5c (1)
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torch.nn.BatchNorm2d.3c1e96c2159ad46 (1)
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torch.nn.BatchNorm2d.6663c31edbb6a02d (1)
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torch.nn.Sequential.ec7abf2430c19828 (1)
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torch.nn.BasicBlock.cf33051c8809a10c (1)
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torch.nn.BatchNorm2d.16d9a89acda5c0d3 (1)
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torch.nn.BatchNorm2d.12fd893df2f61fa (1)
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torch.nn.AdaptiveAvgPool2d.6a21c2ba485854d6 (1)
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torch.nn.BatchNorm2d.e80336c33f386df1 (1)
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torch.nn.Conv2d.d76d161367949e0 (1)
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torch.nn.Linear.794c6c7a90167107 (1)
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torch.nn.Conv2d.2f31dc2b43caecd0 (1)
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torch.nn.Conv2d.ec4dba53889c7f7c (1)
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torch.nn.ReLU.c63433c1e524a9b9 (1)
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torch.nn.BasicBlock.97fe1f88684de846 (1)
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torch.nn.BatchNorm2d.26d423c2a7dd0e93 (1)
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torch.nn.BatchNorm2d.6861e34f9cf779 (1)
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torch.nn.Conv2d.30dc5536b53ea7ca (1)
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torch.nn.Sequential.bc57e6ad340a969c (1)
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torch.nn.BasicBlock.e61a2ef6c32e398a (1)
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torch.nn.BatchNorm2d.2d033b3b36d48bf3 (1)
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torch.nn.BatchNorm2d.6db0f804e84a5d8e (1)
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torch.nn.Conv2d.916d95a0c961f76b (1)
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torch.nn.BatchNorm2d.31923714f9302c25 (1)
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torch.nn.ReLU.7f1ae8943b67ac3b (1)
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torch.nn.Conv2d.e4f866fef2257231 (1)
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torch.nn.Conv2d.ec18d82d4a00be91 (1)
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torch.nn.ReLU.b592304f9d7a849e (1)
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torch.nn.BasicBlock.1fb507e731bddc1c (1)
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torch.nn.BatchNorm2d.2740d97d669ccf49 (1)
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torch.nn.BatchNorm2d.ef1d790134a0ea9a (1)
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torch.nn.Conv2d.d7f8c1af4150fb8f (1)
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torch.nn.ReLU.2361c7a32c71e3ae (1)
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torch.nn.Sequential.d147a5a1976e4c29 (1)
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torch.nn.Conv2d.7bf2aa6f1fdee112 (1)
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torch.nn.Conv2d.fdd7cd563ab5bdf7 (1)
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torch.nn.Sequential.7f2deceab8be82d1 (1)
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torch.nn.Conv2d.49d0fc5960043bb1 (1)
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torch.nn.Conv2d.195fa8c959a56a7 (1)
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torch.nn.Sequential.786d6b6c615f52dd (1)
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torch.nn.Conv2d.9b7d934461fe7406 (1)
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torch.nn.BatchNorm2d.9ff4f6352bac1e00 (1)
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torch.nn.ReLU.c82bc1cef96af656 (1)
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torch.nn.MaxPool2d.34ef56e2731f786 (1)
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torch.nn.ReLU.624703c1ba1b7161 (1)
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torch.nn.ResNet.932e4aa09df54bb6 (1)
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torch.nn.BasicBlock.a8e6a84dda7dc9d5 (1)
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torch.nn.BatchNorm2d.91619b935469d159 (1)
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torch.nn.BatchNorm2d.8585541d406b7a0e (1)
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torch.nn.Conv2d.b2f58de4a710ed30 (1)
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torch.nn.Conv2d.7536cfd5c963e0a2 (1)
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torch.nn.Conv2d.a6587394b16d2f9f (1)
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torch.nn.ReLU.bdbc9ab7aa9c9928 (1)
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torch.nn.BasicBlock.b7929ba93f3b829 (1)
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torch.nn.BatchNorm2d.c7206933cb7e7151 (1)
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torch.nn.BatchNorm2d.95a7fa69ebfada13 (1)
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torch.nn.Conv2d.5ec43c90972cdeed (1)
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torch.nn.ReLU.49d27284b465d564 (1)
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torch.nn.Sequential.c794aefaa49703f3 (1)
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torch.nn.Conv2d.a81e852344277b9c (1)
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torch.nn.Conv2d.299df66b63d11cb2 (1)
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torch.nn.Sequential.e40df2b37c04b686 (1)
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torch.nn.Conv2d.f2eb07c775988d31 (1)
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torch.nn.BatchNorm2d.fce5e936aa1a3 (1)
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torch.nn.ReLU.9fb7beef361dffb9 (1)
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torch.nn.BasicBlock.aa867dc15952ccd (1)
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torch.nn.BatchNorm2d.763535ebf10cf9b7 (1)
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torch.nn.BatchNorm2d.a850d265d121f558 (1)
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torch.nn.AdaptiveAvgPool2d.e48ba31e587dad (1)
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torch.nn.Sequential.bcc79918548636eb (1)
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torch.nn.MBConv.9894589ca38fb9 (1)
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torch.nn.Sequential.3030144c5ad8aef (1)
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torch.nn.Conv2dNormActivation.a8be95b073bab174 (1)
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torch.nn.Sequential.176395b2b4600b3e (1)
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torch.nn.Dropout.cb85095ed7e127bd (1)
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torch.nn.Linear.4dc976a0b7d24523 (1)
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torch.nn.Sequential.629b55d9d96dd8c (1)
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torch.nn.SiLU.25a8183f7143d70c (1)
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torch.nn.Sequential.cf58a70a6140cd41 (1)
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torch.nn.MBConv.b17c57c360fa621c (1)
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torch.nn.Sequential.cbd5a10691dfdf4 (1)
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torch.nn.SqueezeExcitation.1b812084b75246be (1)
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