mmpretrain
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Support Stanford Car dataset
Motivation
Support Stanford Car dataset .
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 Report
Merging #893 (37f0469) into dev (e54cfd6) will increase coverage by
0.03%
. The diff coverage is94.28%
.
:exclamation: Current head 37f0469 differs from pull request most recent head 22b479f. Consider uploading reports for the commit 22b479f to get more accurate results
@@ Coverage Diff @@
## dev #893 +/- ##
==========================================
+ Coverage 85.86% 85.90% +0.03%
==========================================
Files 137 138 +1
Lines 9363 9398 +35
Branches 1621 1627 +6
==========================================
+ Hits 8040 8073 +33
- Misses 1082 1084 +2
Partials 241 241
Flag | Coverage Δ | |
---|---|---|
unittests | 85.82% <94.28%> (+0.03%) |
:arrow_up: |
Flags with carried forward coverage won't be shown. Click here to find out more.
Impacted Files | Coverage Δ | |
---|---|---|
mmcls/datasets/stanford_cars.py | 94.11% <94.11%> (ø) |
|
mmcls/datasets/__init__.py | 100.00% <100.00%> (ø) |
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Hi @zzc98 !First of all, we want to express our gratitude for your significant PR in the MMClassification project. Your contribution is highly appreciated, and we are grateful for your efforts in helping improve this open-source project during your personal time. We believe that many developers will benefit from your PR
We would also like to invite you to join our Special Interest Group (SIG) private channel on Discord, where you can share your experiences, ideas, and build connections with like-minded peers. To join the SIG channel, simply message moderator— OpenMMLab on Discord or briefly share your open-source contributions in the #introductions channel and we will assist you. Look forward to seeing you there! Join us :https://discord.gg/UjgXkPWNqA
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