We would like to thank Ross Girshick, Piotr Dollar, Vaibhav Aggarwal, Francisco Massa and Hu Ye for their past research and contributions to this work.
New pre-trained weights
SWAG weights
The ViT and RegNet model variants offer new pre-trained SWAG (​​Supervised Weakly from hashtAGs) weights. One of the biggest of these models achieves a whopping 88.6% accuracy on ImageNet-1K. We currently offer two versions of the weights: 1) fine-tuned end-to-end weights on ImageNet-1K (highest accuracy) and 2) frozen trunk weights with a linear classifier fit on ImageNet-1K (great for transfer learning). Below we see the detailed accuracies of each model variant:



Model Weights
Acc@1
Acc@5




RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_E2E_V1
86.012
98.054


RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_LINEAR_V1
83.976
97.244


