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@ -33,6 +33,7 @@ And a big thanks to all GitHub sponsors who helped with some of my costs before
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* `maxvit_nano_rw_256` - 82.9 @ 256 (T)
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* `maxvit_nano_rw_256` - 82.9 @ 256 (T)
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* `coatnet_rmlp_1_rw_224` - 83.4 @ 224, 84 @ 320 (T)
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* `coatnet_rmlp_1_rw_224` - 83.4 @ 224, 84 @ 320 (T)
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* `coatnet_1_rw_224` - 83.6 @ 224 (G)
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* `coatnet_1_rw_224` - 83.6 @ 224 (G)
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* (T) = TPU trained with `bits_and_tpu` branch training code, (G) = GPU trained
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* GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% `timm` re-write for license purposes)
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* GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% `timm` re-write for license purposes)
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* MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit)
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* MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit)
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* EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer)
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* EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer)
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