[Doc] Refine algorithm readme with model performance table

[ly015]

Motivation

Update algorithm readme with model performance table and page links for algorithms for better user experience.

Modification

BC-breaking (Optional)

Use cases (Optional)

Checklist

Before PR:

• [ ] I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
• [ ] Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
• [ ] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
• [ ] New functionalities are covered by complete unit tests. If not, please add more unit tests to ensure correctness.
• [ ] The documentation has been modified accordingly, including docstring or example tutorials.

After PR:

• [ ] CLA has been signed and all committers have signed the CLA in this PR.

[Tau-J]

Do you plan to use this page as an alternative of current algorithm docs? I suggest adding it into the docs instead of placing under the config folder. Besides, maybe an accuracy-sorted version is also necessary.

[ly015]

Do you plan to use this page as an alternative of current algorithm docs? I suggest adding it into the docs instead of placing under the config folder. Besides, maybe an accuracy-sorted version is also necessary.

This page is expected to serve as an index for users navigating the configs folder on GitHub. We can modify the doc compiling script to automatically involves these pages in the model zoo document page, or even generate a figure with the table content. That can be a future plan.

As for the sorting, one concern is that the performance comparison may not be completely fair (network scale, input size, ...) so the sorted results could be misleading in some cases. Maybe sort them by the publishing year?

[Tau-J]

Do you plan to use this page as an alternative of current algorithm docs? I suggest adding it into the docs instead of placing under the config folder. Besides, maybe an accuracy-sorted version is also necessary.

This page is expected to serve as an index for users navigating the configs folder on GitHub. We can modify the doc compiling script to automatically involves these pages in the model zoo document page, or even generate a figure with the table content. That can be a future plan.

As for the sorting, one concern is that the performance comparison may not be completely fair (network scale, input size, ...) so the sorted results could be misleading in some cases. Maybe sort them by the publishing year?

I understand your concern. But popular repos like timm and paperwithcodes, whose list is also not completely fair.

So this kind of ranking is just for convenience, and you know, users are always wondering "which one is the best model in your repo"

[codecov[bot]]

Codecov Report

Base: 77.73% // Head: 77.77% // Increases project coverage by +0.04% :tada:

Coverage data is based on head (43a98fb) compared to base (4693395). Patch has no changes to coverable lines.

Additional details and impacted files

@@             Coverage Diff             @@
##           dev-1.x    #1627      +/-   ##
===========================================
+ Coverage    77.73%   77.77%   +0.04%     
===========================================
  Files          204      204              
  Lines        11716    11716              
  Branches      1956     1956              
===========================================
+ Hits          9107     9112       +5     
+ Misses        2213     2206       -7     
- Partials       396      398       +2     

Flag	Coverage Δ
unittests	77.77% <ø> (+0.04%)	:arrow_up:

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Impacted Files	Coverage Δ
mmpose/datasets/transforms/common_transforms.py	83.47% <0.00%> (+1.42%)	:arrow_up:

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