Model card for resnet18d.ra2 in1k A ResNet D image classification model. This model features: ReLU activations 3 layer stem of 3x3 convolutions with pooling 2x2 average pool + 1x1 convolution shortcut downsample Trained on ImageNet 1k in timm using recipe template described below. Recipe details: RandAugment RA2 recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as B recipe in ResNet Strikes Back. RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging Step (exponential decay w/ staircase) LR schedule with warmup Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 11.7 GMACs: 2.1 Activations (M): 3.3 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 Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187 Original: https://github.com/huggingface/pytorch image models Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison Explore the dataset and runtime…
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