Model card for resnext50 32x4d.a1h in1k A ResNeXt B image classification model. This model features: ReLU activations single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample grouped 3x3 bottleneck convolutions Trained on ImageNet 1k in timm using recipe template described below. 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 Details 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 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…
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