Model card for regnety 002.pycls in1k A RegNetY 200MF image classification model. Pretrained on ImageNet 1k by paper authors. The timm RegNet implementation includes a number of enhancements not present in other implementations, including: stochastic depth gradient checkpointing layer wise LR decay configurable output stride (dilation) configurable activation and norm layers option for a pre activation bottleneck block used in RegNetV variant only known RegNetZ model definitions with pretrained weights Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 3.2 GMACs: 0.2 Activations (M): 2.2 Image size: 224 x 224 Papers: Designing Network Design Spaces: https://arxiv.org/abs/2003.13678 Dataset: ImageNet 1k Original: https://github.com/facebookresearch/pycls Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison Explore the dataset and runtime metrics of this model in timm model results. For the comparison summary below, the ra in1k, ra3 in1k, ch in1k, sw , and lion tagged weights are trained in timm . model img size top1 top5 param count gmacs macts regnety 1280.swag ft in1k 384 88.228 98.684 644.81 374.99 210.2…
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