HyperAI超神経
ホーム
ニュース
最新論文
チュートリアル
データセット
百科事典
SOTA
LLMモデル
GPU ランキング
学会
検索
サイトについて
日本語
HyperAI超神経
Toggle sidebar
サイトを検索…
⌘
K
ホーム
SOTA
Long Tail Learning
Long Tail Learning On Coco Mlt
Long Tail Learning On Coco Mlt
評価指標
Average mAP
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Average mAP
Paper Title
Repository
DB Focal(ResNet-50)
53.55
Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
PG Loss(ResNet-50)
54.43
Probability Guided Loss for Long-Tailed Multi-Label Image Classification
-
LDAM(ResNet-50)
40.53
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
RS(ResNet-50)
46.97
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
ML-GCN(ResNet-50)
44.24
Multi-Label Image Recognition with Graph Convolutional Networks
Focal Loss(ResNet-50)
49.46
Focal Loss for Dense Object Detection
LMPT(ResNet-50)
58.97
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
OLTR(ResNet-50)
45.83
Large-Scale Long-Tailed Recognition in an Open World
LTML(ResNet-50)
56.90
Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings
-
CB Loss(ResNet-50)
49.06
Class-Balanced Loss Based on Effective Number of Samples
CLIP(ViT-B/16)
60.17
Learning Transferable Visual Models From Natural Language Supervision
CLIP(ResNet-50)
56.19
Learning Transferable Visual Models From Natural Language Supervision
LMPT(ViT-B/16)
66.19
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
0 of 13 row(s) selected.
Previous
Next