[ReProduce] ReProduce RepVGG Training Accuracy

[Ezra-Yu]

Motivation

ReProduce RepVGG Training Accuracy

• Update checkpoints;
• update configs
• update readme

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: 88.61% // Increases project coverage by +88.59% 🎉

Coverage data is based on head (192431e) compared to base (b8b31e9).
Patch has no changes to coverable lines.

❗ Current head 192431e differs from pull request most recent head 7fb63a2. Consider uploading reports for the commit 7fb63a2 to get more accurate results

Additional details and impacted files

@@             Coverage Diff              @@
##           dev-1.x    #1264       +/-   ##
============================================
+ Coverage     0.02%   88.61%   +88.59%     
============================================
  Files          121      151       +30     
  Lines         8217    11808     +3591     
  Branches      1368     1892      +524     
============================================
+ Hits             2    10464    +10462     
+ Misses        8215     1041     -7174     
- Partials         0      303      +303     

Flag	Coverage Δ
unittests	88.61% <ø> (+88.59%)	⬆️

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/models/backbones/swin_transformer_v2.py	89.63% <0.00%> (ø)
mmcls/engine/hooks/switch_recipe_hook.py	88.46% <0.00%> (ø)
mmcls/models/backbones/efficientformer.py	95.08% <0.00%> (ø)
mmcls/models/backbones/deit3.py	94.52% <0.00%> (ø)
mmcls/models/backbones/mobilevit.py	91.15% <0.00%> (ø)
mmcls/models/backbones/edgenext.py	95.20% <0.00%> (ø)
mmcls/engine/hooks/retriever_hooks.py	72.72% <0.00%> (ø)
mmcls/models/heads/efficientformer_head.py	93.10% <0.00%> (ø)
mmcls/models/retrievers/image2image.py	90.90% <0.00%> (ø)
... and 142 more

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