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A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k.
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A CSP-DarkNet (Cross-Stage-Partial) image classification model. Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * RandAugment `RA` 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
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A DeiT image classification model. Trained on ImageNet-1k using distillation tokens by paper authors.
A DeiT image classification model. Trained on ImageNet-1k using distillation tokens by paper authors.
- **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.6 - GMACs: 17.6 - Activations (M): 23.9 - Image size: 224 x 224 - **Papers:** - Training data-efficient image transformers & distillation through attention: https://arxiv.org/abs/2012.12877 - **Original:** https://github.com/facebookresearch/deit - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.
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- **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 22.1 - GMACs: 4.6 - Activations (M): 11.9 - Image size: 224 x 224 - **Papers:** - Training data-efficient image transformers & distillation through attention: https://arxiv.org/abs/2012.12877 - **Original:** https://github.com/facebookresearch/deit - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.
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