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--- |
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tags: |
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- image-classification |
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- timm |
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library_tag: timm |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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- imagenet-12k |
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--- |
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# Model card for convnext_small.in12k_ft_in1k_384 |
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A ConvNeXt image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. |
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ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. |
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Fine-tuning performed on 8x GPU [Lambda Labs](https://lambdalabs.com/) cloud instances. |
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## Model Details |
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- **Model Type:** Image classification / feature backbone |
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- **Model Stats:** |
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- Params (M): 50.2 |
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- GMACs: 25.6 |
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- Activations (M): 63.4 |
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- Image size: 384 x 384 |
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- **Dataset:** ImageNet-1k |
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- **Pretrain Dataset:** ImageNet-12k |
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- **Papers:** |
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- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 |
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## Citation |
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``` |
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@misc{rw2019timm, |
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author = {Ross Wightman}, |
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title = {PyTorch Image Models}, |
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year = {2019}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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doi = {10.5281/zenodo.4414861}, |
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howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} |
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} |
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``` |
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``` |
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@article{liu2022convnet, |
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author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, |
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title = {A ConvNet for the 2020s}, |
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journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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year = {2022}, |
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} |
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``` |
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