Model card for repvgg a0 A RepVGG image classification model. Trained on ImageNet 1k by paper authors. This model architecture is implemented using timm 's flexible BYOBNet (Bring Your Own Blocks Network). BYOBNet allows configuration of: block / stage layout stem layout output stride (dilation) activation and norm layers channel and spatial / self attention layers ...and also includes timm features common to many other architectures, including: stochastic depth gradient checkpointing layer wise LR decay per stage feature extraction Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 9.1 GMACs: 1.5 Activations (M): 3.6 Image size: 224 x 224 Papers: RepVGG: Making VGG style ConvNets Great Again: https://arxiv.org/abs/2101.03697 Dataset: ImageNet 1k Original: https://github.com/DingXiaoH/RepVGG Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison Explore the dataset and runtime metrics of this model in timm model results. Citation
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