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- **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 5.7 - GMACs: 1.3 - Activations (M): 6.0 - 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.

Image classification
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A DenseNet image classification model. Pretrained on ImageNet-1k in `timm` by Ross Wightman using RandAugment `RA` recipe. Related to `B` recipe in ResNet Strikes Back. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 8.0 - GMACs: 2.9 - Activations (M): 6.9 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - Densely Connected Convolutional Networks: https://arxiv.org/abs/1608.06993 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models

Image classification
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A DenseNet image classification model. Trained on ImageNet-1k (original torchvision weights). - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 8.0 - GMACs: 2.9 - Activations (M): 6.9 - Image size: 224 x 224 - **Papers:** - Densely Connected Convolutional Networks: https://arxiv.org/abs/1608.06993 - **Dataset:** ImageNet-1k - **Original:** https://github.com/pytorch/vision

Image classification
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A DenseNet image classification model. Trained on ImageNet-1k (original torchvision weights). - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.7 - GMACs: 7.8 - Activations (M): 11.1 - Image size: 224 x 224 - **Papers:** - Densely Connected Convolutional Networks: https://arxiv.org/abs/1608.06993 - **Dataset:** ImageNet-1k - **Original:** https://github.com/pytorch/vision

Image classification
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A DenseNet image classification model. Trained on ImageNet-1k (original torchvision weights). - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 20.0 - GMACs: 4.3 - Activations (M): 7.9 - Image size: 224 x 224 - **Papers:** - Densely Connected Convolutional Networks: https://arxiv.org/abs/1608.06993 - **Dataset:** ImageNet-1k - **Original:** https://github.com/pytorch/vision

Image classification
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A DenseNet image classification model. Pretrained on ImageNet-1k in `timm` by Ross Wightman using RandAugment `RA` recipe. Related to `B` recipe in ResNet Strikes Back. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 8.0 - GMACs: 3.1 - Activations (M): 7.9 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - Densely Connected Convolutional Networks: https://arxiv.org/abs/1608.06993 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models

Image classification
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