[tune](deps): Bump timm from 0.4.5 to 0.5.4 in /python/requirements/ml #49
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Bumps [timm](https://github.com/rwightman/pytorch-image-models) from 0.4.5 to 0.5.4. - [Release notes](https://github.com/rwightman/pytorch-image-models/releases) - [Changelog](https://github.com/rwightman/pytorch-image-models/blob/master/docs/archived_changes.md) - [Commits](huggingface/pytorch-image-models@v0.4.5...v0.5.4) --- updated-dependencies: - dependency-name: timm dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <[email protected]>
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Bumps timm from 0.4.5 to 0.5.4.
Commits
e0c4eecDefault conv_mlp to False across the board for ConvNeXt, causing issues on mo...b669f4aAdd ConvNeXt 22k->1k fine-tuned and 384 22k-1k fine-tuned weights after testing6dcbaf2Update README.mda8d103eGiant/gigantic vits snuck through in a test a broke GitHub test runner, add f...ef72ad4Extra vit_huge model likely to cause test issue (non in21k variant), adding t...e967c72Update REAMDE.md. Sneak in g/G (giant / gigantic?) ViT defs from scaling paper9ca3437Add some more small model weights lcnet, mnas, mnv2fa6463cVersion 0.5.4fa81164Fix stem width for really small mobilenetv3 arch defsedd3d73Add missing dropout for head reset in ConvNeXt default headDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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