Classification On N Imagenet
Metrics
Accuracy (%)
Results
Performance results of various models on this benchmark
Model Name | Accuracy (%) | Paper Title | Repository |
|---|---|---|---|
| DiST | 48.43 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| Time Surface | 44.32 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| Binary Event Image | 46.36 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| Timestamp Image | 45.86 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| Event Image | 45.77 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| HATS | 47.14 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| Sorted Time Surface | 47.90 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| Event Spike Tensor | 48.93 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras | |
| Event Histogram | 47.73 | N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras |
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