predictions = m_segmenter(x)
These models are highly competitive with TorchVision’s existing models on resource efficiency, speed, and accuracy. See our [release notes](https://github.com/pytorch/vision/releases) for detailed performance metrics.

### [Stable] AutoAugment
[AutoAugment](https://arxiv.org/pdf/1805.09501.pdf) is a common Data Augmentation technique that can increase the accuracy of Scene Classification models. Though the data augmentation policies are directly linked to their trained dataset, empirical studies show that ImageNet policies provide significant improvements when applied to other datasets. We’ve implemented 3 policies learned on the following datasets: ImageNet, CIFA10 and SVHN. These can be used standalone or mixed-and-matched with existing transforms:
```python
from torchvision import transforms

t = transforms.AutoAugment()
transformed = t(image)


transform=transforms.Compose([
   transforms.Resize(256),
   transforms.AutoAugment(),
   transforms.ToTensor()])
