# Image Classification on CIFAR-10 (Accuracy, AUC, NLL)

Source: [Image Classification on CIFAR-10 (Accuracy, AUC, NLL)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-accuracy-auc-nll>)

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

Data updated: 2026-03-27T12:40:15.369Z

Evaluation of Image Classification performance on the CIFAR-10 dataset, reporting standard classification metrics including Accuracy, Area Under the Curve (AUC), and Negative Log-Likelihood (NLL).

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 | Image Classification |
| Dataset (including any stated split) | CIFAR-10 |
| Metric | Accuracy |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 4 of 4 |
| 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/image-classification-on-cifar-10-accuracy-auc-nll.md?metric=Accuracy>): Higher is better; unit: PERCENT.

- [AUC](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-accuracy-auc-nll.md?metric=AUC>): Higher is better; unit: PERCENT.

- [NLL](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-accuracy-auc-nll.md?metric=NLL>): Lower is better; unit: SCALAR.

## Recorded results

| Method | Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| Nesterov-SDEBNN | 88.36 | Not recorded | 69c60ef40636e5bf4afc8443 |
| SDE-BNN | 87.56 | Not recorded | 69c60ef40636e5bf4afc8443 |
| Nesterov-SDEBNN | 86.99 | Not recorded | 69c60ef40636e5bf4afc8443 |
| SDE-BNN | 85.87 | Not recorded | 69c60ef40636e5bf4afc8443 |

### Nesterov-SDEBNN

Accuracy: 88.36. Result ID: 69c67a9f47f34ce924b05b8a.

[Reporting paper](<https://www.sota2.com/research/paper/69c60ef40636e5bf4afc8443.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69c60ef40636e5bf4afc8443.md>)

Recorded evaluation configuration: {"Integration scheme":"fixed-step"}. This record does not assert independent reproduction.

### SDE-BNN

Accuracy: 87.56. Result ID: 69c67a9f47f34ce924b05b89.

[Reporting paper](<https://www.sota2.com/research/paper/69c60ef40636e5bf4afc8443.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6bba1e209131cea2fed5.md>)

Recorded evaluation configuration: {"Integration scheme":"fixed-step"}. This record does not assert independent reproduction.

### Nesterov-SDEBNN

Accuracy: 86.99. Result ID: 69c67a9f47f34ce924b05b8c.

[Reporting paper](<https://www.sota2.com/research/paper/69c60ef40636e5bf4afc8443.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69c60ef40636e5bf4afc8443.md>)

Recorded evaluation configuration: {"Integration scheme":"adaptive-step"}. This record does not assert independent reproduction.

### SDE-BNN

Accuracy: 85.87. Result ID: 69c67a9f47f34ce924b05b8b.

[Reporting paper](<https://www.sota2.com/research/paper/69c60ef40636e5bf4afc8443.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6bba1e209131cea2fed5.md>)

Recorded evaluation configuration: {"Integration scheme":"adaptive-step"}. This record does not assert independent reproduction.

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