**Highlights**
[[BETA](https://pytorch.org/blog/pytorch-feature-classification-changes/#beta)] New Model Registration API
Following up on the [multi-weight support API](https://pytorch.org/blog/introducing-torchvision-new-multi-weight-support-api/) that was released on the previous version, we have added a new [model registration API](https://pytorch.org/blog/easily-list-and-initialize-models-with-new-apis-in-torchvision/) to help users retrieve models and weights. There are now 4 new methods under the `torchvision.models` module: `get_model`, `get_model_weights`, `get_weight`, and `list_models`. Here are examples of how we can use them:
python
import torchvision
from torchvision.models import get_model, get_model_weights, list_models
max_params = 5000000
tiny_models = []
for model_name in list_models(module=torchvision.models):
weights_enum = get_model_weights(model_name)
if len([w for w in weights_enum if w.meta["num_params"] <= max_params]) > 0:
tiny_models.append(model_name)
print(tiny_models)
['mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mobilenet_v2', ...]
model = get_model(tiny_models[0], weights="DEFAULT")
print(sum(x.numel() for x in model.state_dict().values()))
2239188
As of now, this API is still [beta](https://pytorch.org/blog/pytorch-feature-classification-changes/#beta) and there might be changes in the future in order to improve its usability based on your [feedback](https://github.com/pytorch/vision/issues/6365).
New Architecture and Model Variants
Classification Models
We’ve added the Swin Transformer V2 architecture along with pre-trained weights for its tiny/small/base variants. In addition, we have added support for the MaxViT transformer. Here is an example on how to use the models:
python
import torch
from torchvision.models import *
image = torch.rand(1, 3, 224, 224)
model = swin_v2_t(weights="DEFAULT").eval()
model = maxvit_t(weights="DEFAULT").eval()
prediction = model(image)
Here is the table showing the accuracy of the models tested on ImageNet1K dataset.
<table>
<tr>
<td><strong>Model</strong>
</td>
<td><strong>Acc1</strong>
</td>
<td><strong>Acc1</strong>
<p>
<strong>change over V1</strong>
</td>
<td><strong>Acc5</strong>
</td>
<td><strong>Acc5</strong>
<p>
<strong>change over V1</strong>
</td>
</tr>
<tr>
<td>swin_v2_t
</td>
<td><p style="text-align: right">