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This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample * grouped 3x3 bottleneck convolutions Recipe details: * Based on ResNet Strikes Back `A1` recipe * LAMB optimizer * Stronger dropout, stochastic depth, and RandAugment than paper `A1` recipe * Cosine LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 25.0 - GMACs: 4.3 - Activations (M): 14.4 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Aggregated Residual Transformations for Deep Neural Networks: https://arxiv.org/abs/1611.05431 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/huggingface/pytorch-image-models

Image classification
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- **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 9.7 - GMACs: 0.9 - Activations (M): 11.2 - Image size: 224 x 224 - **Papers:** - Rethinking Channel Dimensions for Efficient Model Design: https://arxiv.org/abs/2007.00992 - **Original:** https://github.com/clovaai/rexnet - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results."

Image classification
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