# Image Classification on Food-11

Source: [Image Classification on Food-11](<https://www.sota2.com/research/sota/image-classification-on-food-11>)

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

Data updated: 2026-03-20T08:13:17.793Z

Evaluation of Image Classification performance on the Food-11 dataset, reporting Top-1 and Top-5 accuracy metrics.

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) | Food-11 |
| Metric | Top-1 Accuracy |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 5 of 5 |
| 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

- [Top-1 Accuracy](<https://www.sota2.com/research/sota/image-classification-on-food-11.md?metric=Top-1%20Accuracy>): Higher is better; unit: PERCENT.

- [Top-5 Accuracy](<https://www.sota2.com/research/sota/image-classification-on-food-11.md?metric=Top-5%20Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Top-1 Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| InfoMamba | 91 | Not recorded | 69bcd4880636e5bf4afc29b9 |
| VGG19 | 81.32 | Not recorded | 69bcd4880636e5bf4afc29b9 |
| ResNet50 | 80.56 | Not recorded | 69bcd4880636e5bf4afc29b9 |
| MobileNetV2 | 73.69 | Not recorded | 69bcd4880636e5bf4afc29b9 |
| AlexNet | 69.72 | Not recorded | 69bcd4880636e5bf4afc29b9 |

### InfoMamba

Top-1 Accuracy: 91. Result ID: 69bd019447f34ce924a6ee69.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4880636e5bf4afc29b9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69bcd4880636e5bf4afc29b9.md>)

Recorded evaluation configuration: {"Params (M)":"≈100","GFLOPs":"≈18.5"}. This record does not assert independent reproduction.

### VGG19

Top-1 Accuracy: 81.32. Result ID: 69bd019447f34ce924a6ee65.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4880636e5bf4afc29b9.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: {"Params (M)":"143.7","GFLOPs":"19.6"}. This record does not assert independent reproduction.

### ResNet50

Top-1 Accuracy: 80.56. Result ID: 69bd019447f34ce924a6ee66.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4880636e5bf4afc29b9.md>)

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

Recorded evaluation configuration: {"Params (M)":"25.6","GFLOPs":"4.1"}. This record does not assert independent reproduction.

### MobileNetV2

Top-1 Accuracy: 73.69. Result ID: 69bd019447f34ce924a6ee67.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4880636e5bf4afc29b9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa65811e209131cea0a9a1.md>)

[Implementation](<https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet>)

Recorded evaluation configuration: {"Params (M)":"3.5","GFLOPs":"0.3"}. This record does not assert independent reproduction.

### AlexNet

Top-1 Accuracy: 69.72. Result ID: 69bd019447f34ce924a6ee68.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4880636e5bf4afc29b9.md>)

Recorded evaluation configuration: {"Params (M)":"61.0","GFLOPs":"0.7"}. This record does not assert independent reproduction.

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