[Enhance] Reproduce mobileone training accuracy

[Ezra-Yu]

Motivation

Reproduce mobileone training accuracy

base on #1101

Modification

1. Update configs, readme, and link.
2. update vis_scheduler.pt tool to visualize weight-decay plot

Use cases (Optional)

Train

python tools/train.py configs/mobileone/mobileone-s0_8xb32_in1k.py

visualize weight decay plot

python ./tools/visualizations/vis_scheduler.py configs/mobileone/mobileone-s0_8xb32_in1k.py  -d 1000 -p momentum

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: 89.08% // Increases project coverage by +89.06% 🎉

Coverage data is based on head (957ddfa) compared to base (b8b31e9).
Patch has no changes to coverable lines.

❗ Current head 957ddfa differs from pull request most recent head 220816e. Consider uploading reports for the commit 220816e to get more accurate results

Additional details and impacted files

@@             Coverage Diff              @@
##           dev-1.x    #1191       +/-   ##
============================================
+ Coverage     0.02%   89.08%   +89.06%     
============================================
  Files          121      143       +22     
  Lines         8217    10885     +2668     
  Branches      1368     1731      +363     
============================================
+ Hits             2     9697     +9695     
+ Misses        8215      947     -7268     
- Partials         0      241      +241     

Flag	Coverage Δ
unittests	89.08% <ø> (+89.06%)	⬆️

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

Impacted Files	Coverage Δ
mmcls/apis/inference.py	0.00% <0.00%> (ø)
mmcls/datasets/transforms/compose.py
mmcls/models/classifiers/hugging_face.py	25.33% <0.00%> (ø)
mmcls/models/backbones/mobilevit.py	91.15% <0.00%> (ø)
mmcls/engine/hooks/retriever_hooks.py	72.72% <0.00%> (ø)
mmcls/models/retrievers/image2image.py	92.38% <0.00%> (ø)
mmcls/engine/hooks/switch_recipe_hook.py	88.46% <0.00%> (ø)
mmcls/structures/utils.py	77.77% <0.00%> (ø)
mmcls/models/backbones/swin_transformer_v2.py	89.63% <0.00%> (ø)
mmcls/models/classifiers/timm.py	25.67% <0.00%> (ø)
... and 133 more

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