Classification
The Swin Transformer and EfficienetNetV2 are two popular classification models which are often used for downstream vision tasks. This release includes 6 pre-trained weights for their classification variants. Here is how to use the new models:
import torch
from torchvision.models import *

image = torch.rand(1, 3, 224, 224)
model = swin_t(weights="DEFAULT").eval()
prediction = model(image)

image = torch.rand(1, 3, 384, 384)
model = efficientnet_v2_s(weights="DEFAULT").eval()
prediction = model(image)

In addition to the above, we also provide new variants for existing architectures such as ShuffleNetV2, ResNeXt and MNASNet. The accuracies of all the new pre-trained models obtained on ImageNet-1K are seen below:



Model
Acc@1
Acc@5




swin_t
81.474
95.776


