[Reproduce] Update ConvNeXt config files.

[mzr1996]

Motivation

I have reproduced the accuracy of ConvNeXt's tiny, small and base architectures.

Modification

Update all ConvNeXt configs. And since the ConvNeXt-base accuracy is a little lower than the original accuracy (83.66 vs 83.85), I reserved the 3rd party ConvNeXt-base checkpoint.

Checklist

Before PR:

• Pre-commit or other linting tools are used to fix the potential lint issues.
• Bug fixes are fully covered by unit tests, the case that causes the bug should be added in the unit tests.
• The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
• The documentation has been modified accordingly, like docstring or example tutorials.

After PR:

• If the modification has potential influence on downstream or other related projects, this PR should be tested with those projects, like MMDet or MMSeg.
• CLA has been signed and all committers have signed the CLA in this PR.

[codecov]

Codecov Report

Base: 0.02% // Head: 89.17% // Increases project coverage by +89.15% 🎉

Coverage data is based on head (4193b4d) compared to base (b8b31e9).
Patch has no changes to coverable lines.

Additional details and impacted files

@@             Coverage Diff              @@
##           dev-1.x    #1256       +/-   ##
============================================
+ Coverage     0.02%   89.17%   +89.15%     
============================================
  Files          121      150       +29     
  Lines         8217    11549     +3332     
  Branches      1368     1850      +482     
============================================
+ Hits             2    10299    +10297     
+ Misses        8215      967     -7248     
- Partials         0      283      +283     

Flag	Coverage Δ
unittests	89.17% <ø> (+89.15%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
mmcls/datasets/transforms/compose.py
mmcls/evaluation/metrics/voc_multi_label.py	100.00% <0.00%> (ø)
mmcls/models/necks/reduction.py	100.00% <0.00%> (ø)
mmcls/engine/hooks/switch_recipe_hook.py	88.46% <0.00%> (ø)
mmcls/models/backbones/replknet.py	93.00% <0.00%> (ø)
mmcls/engine/hooks/retriever_hooks.py	72.72% <0.00%> (ø)
mmcls/models/backbones/edgenext.py	95.20% <0.00%> (ø)
mmcls/engine/hooks/ema_hook.py	81.69% <0.00%> (ø)
mmcls/models/backbones/efficientformer.py	95.08% <0.00%> (ø)
mmcls/models/classifiers/hugging_face.py	25.64% <0.00%> (ø)
... and 141 more

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[tonysy]

#996

[yingfhu]

LGTM