Model card for resnet18.a1 in1k A ResNet B image classification model. This model features: ReLU activations single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample Trained on ImageNet 1k in timm using recipe template described below. Recipe details: ResNet Strikes Back A1 recipe LAMB optimizer with BCE loss Cosine LR schedule with warmup Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 11.7 GMACs: 1.8 Activations (M): 2.5 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 Original: https://github.com/huggingface/pytorch image models Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison Explore the dataset and runtime metrics of this model in timm model results. model img size top1 top5 param count gmacs macts img/sec seresnextaa101d 32x8d.sw in12k ft in1k 288 320 86.72 98.17 93.6 35.2 69.7 451 seresnextaa101d 32x8d.sw in12k ft in1k 288 288 86.51 98.08 93.6 28.5 56.4 560 seresnextaa101d 32x8d.sw in12k ft in1k…
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