# Image Classification on PlantVillage 1-shot (test)

Source: [Image Classification on PlantVillage 1-shot (test)](<https://www.sota2.com/research/sota/image-classification-on-plantvillage-1-shot-test>)

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

Data updated: 2026-02-26T05:31:52.679Z

Evaluation of standard Image Classification performance on the 1-shot test split of the PlantVillage dataset.

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) | PlantVillage 1-shot (test) |
| 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-plantvillage-1-shot-test.md?metric=Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| Ensemble MaxViT, ConvNeXt, Swin Transformer | 49.08 | Not recorded | 6940e7ba456146936284fa3b |
| Ensemble MaxViT, ConvNeXt, Swin Transformer | 46.87 | Not recorded | 6940e7ba456146936284fa3b |
| Ensemble MaxViT, ConvNeXt, Swin Transformer | 40.17 | Not recorded | 6940e7ba456146936284fa3b |
| Ensemble 5 ResNets 4 DenseNets | 37.89 | Not recorded | 6940e7ba456146936284fa3b |

### Ensemble MaxViT, ConvNeXt, Swin Transformer

Accuracy: 49.08. Result ID: 699846655698665ec4a3297e.

[Reporting paper](<https://www.sota2.com/research/paper/6940e7ba456146936284fa3b.md>)

[Method origin paper](<https://www.sota2.com/research/paper/6940e7ba456146936284fa3b.md>)

Recorded evaluation configuration: {"Feature Extractor":"Ensemble MaxViT, ConvNeXt, Swin Transformer","Total Model Size (MB)":"500.3","GFLOPS":"22.98","Classifier":"Bi-LSTM"}. This record does not assert independent reproduction.

### Ensemble MaxViT, ConvNeXt, Swin Transformer

Accuracy: 46.87. Result ID: 699846655698665ec4a3297d.

[Reporting paper](<https://www.sota2.com/research/paper/6940e7ba456146936284fa3b.md>)

[Method origin paper](<https://www.sota2.com/research/paper/6940e7ba456146936284fa3b.md>)

Recorded evaluation configuration: {"Feature Extractor":"Ensemble MaxViT, ConvNeXt, Swin Transformer","Total Model Size (MB)":"500.3","GFLOPS":"22.98","Classifier":"LSTM"}. This record does not assert independent reproduction.

### Ensemble MaxViT, ConvNeXt, Swin Transformer

Accuracy: 40.17. Result ID: 699846655698665ec4a3297c.

[Reporting paper](<https://www.sota2.com/research/paper/6940e7ba456146936284fa3b.md>)

[Method origin paper](<https://www.sota2.com/research/paper/6940e7ba456146936284fa3b.md>)

Recorded evaluation configuration: {"Feature Extractor":"Ensemble MaxViT, ConvNeXt, Swin Transformer","Total Model Size (MB)":"500.3","GFLOPS":"22.98","Classifier":"Dense"}. This record does not assert independent reproduction.

### Ensemble 5 ResNets 4 DenseNets

Accuracy: 37.89. Result ID: 699846655698665ec4a3297b.

[Reporting paper](<https://www.sota2.com/research/paper/6940e7ba456146936284fa3b.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6a981e209131cea29317.md>)

Recorded evaluation configuration: {"Feature Extractor":"Ensemble 5 ResNets 4 DenseNets","Total Model Size (MB)":"900.01","GFLOPS":"47.01","Classifier":"Dense"}. This record does not assert independent reproduction.

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