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Image Classification On Cifar 100

Metrics

Percentage correct

Results

Performance results of various models on this benchmark

Paper Title
EffNet-L2 (SAM)96.08Sharpness-Aware Minimization for Efficiently Improving Generalization
Swin-L + ML-Decoder95.1ML-Decoder: Scalable and Versatile Classification Head
µ2Net (ViT-L/16)94.95An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
ViT-B-16 (ImageNet-21K-P pretrain)94.2ImageNet-21K Pretraining for the Masses
CvT-W2494.09CvT: Introducing Convolutions to Vision Transformers
ViT-B/16 (PUGD)93.95Perturbated Gradients Updating within Unit Space for Deep Learning
Heinsen Routing + BEiT-large 16 22493.8An Algorithm for Routing Vectors in Sequences
BiT-L (ResNet)93.51Big Transfer (BiT): General Visual Representation Learning
Astroformer93.36Astroformer: More Data Might not be all you need for Classification
VIT-L/16 (Spinal FC, Background)93.31Reduction of Class Activation Uncertainty with Background Information
CaiT-M-36 U 22493.1-
ViT-L (attn fine-tune)93.0Three things everyone should know about Vision Transformers
TResNet-L-V292.6TResNet: High Performance GPU-Dedicated Architecture
EfficientNetV2-L92.3EfficientNetV2: Smaller Models and Faster Training
EfficientNetV2-M92.2EfficientNetV2: Smaller Models and Faster Training
BiT-M (ResNet)92.17Big Transfer (BiT): General Visual Representation Learning
CeiT-S (384 finetune resolution)91.8Incorporating Convolution Designs into Visual Transformers
CeiT-S91.8Incorporating Convolution Designs into Visual Transformers
EfficientNet-B791.7EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNetV2-S91.5EfficientNetV2: Smaller Models and Faster Training
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