Cross Domain Few Shot Object Detection On 2
Métriques
mAP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | mAP |
|---|---|
| fsce-few-shot-object-detection-via | 21.9 |
| detecting-twenty-thousand-classes-using-image | 15.4 |
| exploring-plain-vision-transformer-backbones | 29.4 |
| balanced-id-ood-tradeoff-transfer-makes-query | 31.1 |
| frustratingly-simple-few-shot-object | 20.5 |
| cross-domain-few-shot-object-detection-via | 30.8 |
| defrcn-decoupled-faster-r-cnn-for-few-shot | 22.9 |
| detect-every-thing-with-few-examples | 25.6 |
| meta-rcnn-meta-learning-for-few-shot-object | 20.6 |