# Image Classification on ImageNet Large-scale unseen classes (test)

Source: [Image Classification on ImageNet Large-scale unseen classes (test)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-large-scale-unseen-classes-test>)

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

Data updated: 2026-02-26T06:06:54.075Z

Evaluation of Image Classification performance on the specific test split derived from ImageNet containing large-scale unseen classes, typically measured using per-class average top-1 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 | Image Classification |
| Dataset (including any stated split) | ImageNet Large-scale unseen classes (test) |
| Metric | Top-1 Acc (Per-Class Avg) |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 0 of 0 |
| 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 Acc (Per-Class Avg)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-large-scale-unseen-classes-test.md?metric=Top-1%20Acc%20(Per-Class%20Avg)>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Top-1 Acc (Per-Class Avg) | Result date | Report paper |
| --- | --- | --- | --- |

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