The DeepNets-1M dataset is composed of neural network architectures represented as graphs where nodes are operations (convolution, pooling, etc.) and edges correspond to the forward pass flow of data through the network. DeepNets-1M has 1 million training architectures and 1402 in-distribution (ID) and out-of-distribution (OOD) evaluation architectures: 500 validation and 500 testing ID architectures, 100 wide OOD architectures, 100 deep OOD architectures, 100 dense OOD architectures, 100 OOD archtectures without batch normalization, and 2 predefined architectures (ResNet-50 and 12 layer Visual Transformer).
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