# Classification on Small rice grain disease dataset (train)

Source: [Classification on Small rice grain disease dataset (train)](<https://www.sota2.com/research/sota/classification-on-small-rice-grain-disease-dataset-train>)

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

Data updated: 2026-02-26T06:07:33.285Z

Evaluation of Classification performance on the training split of the Small rice grain disease dataset, reporting metrics such as Train Loss and Train 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) | Small rice grain disease dataset (train) |
| Metric | Train Loss |
| Unit | SCALAR |
| Direction | Lower is better |
| Results loaded | 3 of 3 |
| 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

- [Train Loss](<https://www.sota2.com/research/sota/classification-on-small-rice-grain-disease-dataset-train.md?metric=Train%20Loss>): Lower is better; unit: SCALAR.

- [Train Accuracy](<https://www.sota2.com/research/sota/classification-on-small-rice-grain-disease-dataset-train.md?metric=Train%20Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Train Loss | Result date | Report paper |
| --- | --- | --- | --- |
| VGG19 | 0.095 | Not recorded | 68fa69e51e209131cea24aac |
| VGG16 | 0.196 | Not recorded | 68fa69e51e209131cea24aac |
| ResNet50 | 0.367 | Not recorded | 68fa69e51e209131cea24aac |

### VGG19

Train Loss: 0.095. Result ID: 698defb3ffc3901de099a707.

[Reporting paper](<https://www.sota2.com/research/paper/68fa69e51e209131cea24aac.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa61d01e209131ce9f62f5.md>)

[Implementation](<http://www.robots.ox.ac.uk/~vgg/research/very_deep/>)

Recorded evaluation configuration: {"CNN Architecture":"VGG19"}. This record does not assert independent reproduction.

### VGG16

Train Loss: 0.196. Result ID: 698defb3ffc3901de099a706.

[Reporting paper](<https://www.sota2.com/research/paper/68fa69e51e209131cea24aac.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa61d01e209131ce9f62f5.md>)

[Implementation](<http://www.robots.ox.ac.uk/~vgg/research/very_deep/>)

Recorded evaluation configuration: {"CNN Architecture":"VGG16"}. This record does not assert independent reproduction.

### ResNet50

Train Loss: 0.367. Result ID: 698defb3ffc3901de099a708.

[Reporting paper](<https://www.sota2.com/research/paper/68fa69e51e209131cea24aac.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa62321e209131ce9f8328.md>)

Recorded evaluation configuration: {"CNN Architecture":"ResNet50"}. This record does not assert independent reproduction.

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