Detect Everything with Few Examples

Benchmark Model Rank Results
cross-domain-few-shot-object-detection-onDE-ViT-FT#4mAP: 49.2
cross-domain-few-shot-object-detection-onDE-ViT(w/o FT)#12mAP: 9.2
cross-domain-few-shot-object-detection-on-1DE-ViT-FT#5mAP: 40.8
cross-domain-few-shot-object-detection-on-1DE-ViT(w/o FT)#10mAP: 11.0
cross-domain-few-shot-object-detection-on-2DE-ViT-FT#5mAP: 25.6
cross-domain-few-shot-object-detection-on-2DE-ViT(w/o FT)#11mAP: 8.4
cross-domain-few-shot-object-detection-on-3DE-ViT-FT#6mAP: 21.3
cross-domain-few-shot-object-detection-on-3DE-ViT(w/o FT)#9mAP: 2.1
cross-domain-few-shot-object-detection-on-4DE-ViT-FT#11mAP: 5.4
cross-domain-few-shot-object-detection-on-4DE-ViT(w/o FT)#12mAP: 3.1
cross-domain-few-shot-object-detection-on-neuDE-ViT-FT#6mAP: 8.8
cross-domain-few-shot-object-detection-on-neuDE-ViT(w/o FT)#9mAP: 1.8
few-shot-object-detection-on-ms-coco-10-shotDE-ViT#3AP: 34.0
few-shot-object-detection-on-ms-coco-30-shotDE-ViT#3AP: 34
one-shot-object-detection-on-cocoDE-ViT#2AP 0.5: 28.4
open-vocabulary-object-detection-on-lvis-v1-0DE-ViT#7AP novel-LVIS base training: 34.3
open-vocabulary-object-detection-on-mscocoDE-ViT#2AP 0.5: 50