# Classification on Stanford Cars 30% labels (test)

Source: [Classification on Stanford Cars 30% labels (test)](<https://www.sota2.com/research/sota/classification-on-stanford-cars-30-percent-labels-test>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:28:20.051Z

Evaluation of Classification performance on the 30% subset of the test split of the Stanford Cars dataset, reporting Accuracy.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Classification |
| Dataset (including any stated split) | Stanford Cars 30% labels (test) |
| Metric | Accuracy |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 8 of 8 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Accuracy](<https://www.sota2.com/research/sota/classification-on-stanford-cars-30-percent-labels-test.md?metric=Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| Self-Tuning+HCR | 87.7 | Not recorded | 68fa6cb81e209131cea358e5 |
| Self-Tuning | 85.87 | Not recorded | 68fa6cb81e209131cea358e5 |
| FixMatch | 77.54 | Not recorded | 68fa6cb81e209131cea358e5 |
| Pseudo-Labeling | 67.02 | Not recorded | 68fa6cb81e209131cea358e5 |
| Mean Teacher | 66.02 | Not recorded | 68fa6cb81e209131cea358e5 |
| UDA | 64.16 | Not recorded | 68fa6cb81e209131cea358e5 |
| SimCLRv2 | 61.7 | Not recorded | 68fa6cb81e209131cea358e5 |
| Pi-model | 57.29 | Not recorded | 68fa6cb81e209131cea358e5 |

### Self-Tuning+HCR

Accuracy: 87.7. Result ID: 698f35413a6c314849f70200.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

### Self-Tuning

Accuracy: 85.87. Result ID: 698f35413a6c314849f701ff.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6a5e1e209131cea27c29.md>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

### FixMatch

Accuracy: 77.54. Result ID: 698f35413a6c314849f701fd.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa68d61e209131cea1e896.md>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

### Pseudo-Labeling

Accuracy: 67.02. Result ID: 698f35413a6c314849f701f9.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

### Mean Teacher

Accuracy: 66.02. Result ID: 698f35413a6c314849f701fb.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa64481e209131cea03b45.md>)

[Implementation](<https://github.com/CuriousAI/mean-teacher>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

### UDA

Accuracy: 64.16. Result ID: 698f35413a6c314849f701fc.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa68a61e209131cea1d69a.md>)

[Implementation](<https://github.com/google-research/uda>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

### SimCLRv2

Accuracy: 61.7. Result ID: 698f35413a6c314849f701fe.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa68a21e209131cea1d4d1.md>)

[Implementation](<https://github.com/google-research/simclr>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

### Pi-model

Accuracy: 57.29. Result ID: 698f35413a6c314849f701fa.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6cb81e209131cea358e5.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa63301e209131ce9fd2e9.md>)

Recorded evaluation configuration: {"Backbone":"ResNet-50","Pre-trained":"ImageNet","Label Proportion":"30%"}. This record does not assert independent reproduction.

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