Cross Domain Few Shot Object Detection On
Métriques
mAP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | mAP |
|---|---|
| detecting-twenty-thousand-classes-using-image | 12.0 |
| frustratingly-simple-few-shot-object | 14.8 |
| defrcn-decoupled-faster-r-cnn-for-few-shot | 15.5 |
| exploring-plain-vision-transformer-backbones | 23.4 |
| cross-domain-few-shot-object-detection-via | 60.5 |
| balanced-id-ood-tradeoff-transfer-makes-query | 53.3 |
| fsce-few-shot-object-detection-via | 15.9 |
| meta-rcnn-meta-learning-for-few-shot-object | 14.0 |
| detect-every-thing-with-few-examples | 49.2 |
| balanced-id-ood-tradeoff-transfer-makes-query | 58.4 |