model = get_model(tiny_models[0], weights="DEFAULT")
print(sum(x.numel() for x in model.state_dict().values()))
2239188

#### (Beta) New Video Classification Models

We added two new video classification models, MViT and S3D. MViT is a state of the art video classification transformer model which has 80.757% accuracy on the Kinetics400 dataset, while S3D is a relatively small model with good accuracy for its size. These models can be used as follows:

```Python
import torch
from torchvision.models.video import *

video = torch.rand(3, 32, 800, 600)
model = mvit_v2_s(weights="DEFAULT")
# model = s3d(weights="DEFAULT")
model.eval()
prediction = model(images)

Here is the table showing the accuracy of the new video classification models tested in the Kinetics400 dataset.



Model
Acc@1
Acc@5




mvit_v1_b
81.474
95.776


