Image Classification On Omnibenchmark

Average Top-1 Accuracy

Results

Performance results of various models on this benchmark

Model Name	Average Top-1 Accuracy	Paper Title
Bamboo-R50	45.4	Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
Adapter-ViTB/16	44.5	Parameter-Efficient Transfer Learning for NLP
BeiT	30.1	BEiT: BERT Pre-Training of Image Transformers
IG-1B	40.4	Billion-scale semi-supervised learning for image classification
InceptionV4	32.3	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
ReLabel	30.8	Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
NOAH-ViTB/16	47.6	Neural Prompt Search
MAE	30.6	Masked Autoencoders Are Scalable Vision Learners
Manifold	31.6	Manifold Mixup: Better Representations by Interpolating Hidden States
MLP-Mixer	32.2	MLP-Mixer: An all-MLP Architecture for Vision
ResNet-101	37.4	Deep Residual Learning for Image Recognition
ResNet-50	34.3	Deep Residual Learning for Image Recognition
MoCoV2	34.8	Momentum Contrast for Unsupervised Visual Representation Learning
SwinTransformer	46.4	Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
MoPro-V2	36.1	MoPro: Webly Supervised Learning with Momentum Prototypes
EfficientNetB4	35.8	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
CLIP-RN50	42.1	Learning Transferable Visual Models From Natural Language Supervision
DINO	38.9	Emerging Properties in Self-Supervised Vision Transformers
BiT-M	40.4	Big Transfer (BiT): General Visual Representation Learning
SwAV	38.3	Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

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