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Fine Grained Image Classification On Oxford 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
OmniVec299.6OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning-
ALIGN96.19%Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision-
AutoAugment88.98%AutoAugment: Learning Augmentation Policies from Data-
TNT-B95.0%Transformer in Transformer-
Bamboo (ViT-B/16)95.1%Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy-
DINOv2 (ViT-g/14, frozen model, linear eval)96.7DINOv2: Learning Robust Visual Features without Supervision-
EfficientNet-B795.4%EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks-
OmniVec99.2OmniVec: Learning robust representations with cross modal sharing-
AutoFormer-S | 38494.9%AutoFormer: Searching Transformers for Visual Recognition-
NAT-M1-Neural Architecture Transfer-
ViT R26 + S/32 ( Augmented)96.28Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detection-
FixSENet-15494.8%Fixing the train-test resolution discrepancy-
SEER (RegNet10B)85.3%Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision-
IELT95.28%Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
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Fine Grained Image Classification On Oxford 1 | SOTA | HyperAI