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Please observe original license. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 4.0 - GMACs: 0.7 - Activations (M): 8.6 - Image size: 256 x 256 - **Papers:** - FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization: https://arxiv.org/abs/2303.14189 - **Original:** https://github.com/apple/ml-fastvit - **Dataset:** ImageNet-1k

Image classification
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A FBNet image classification model. Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * A simple RmsProp based recipe without RandAugment. Using RandomErasing, mixup, dropout, standard random-resize-crop augmentation. * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup

Image classification
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- **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 5.2 - GMACs: 0.1 - Activations (M): 3.5 - Image size: 224 x 224 - **Papers:** - GhostNet: More Features from Cheap Operations: https://arxiv.org/abs/1911.11907 - **Original:** https://github.com/huawei-noah/Efficient-AI-Backbones - **Dataset:** ImageNet-1k

Image classification
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