Model card for seresnext50 32x4d.racm in1k A SE ResNeXt B image classification model with Squeeze and Excitation channel attention. This model features: ReLU activations single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample grouped 3x3 bottleneck convolutions Squeeze and Excitation channel attention Trained on ImageNet 1k in timm using recipe template described below. Recipe details: RandAugment RACM 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): 27.6 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 Squeeze and Excitation Networks: https://arxiv.org/abs/1709.01507 Original: https…
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