You can load and use pre-trained detection and segmentation models with a few lines of code
import torchvision

model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
# set it to evaluation mode, as the model behaves differently
# during training and during evaluation
model.eval()

image = PIL.Image.open('/path/to/an/image.jpg')
image_tensor = torchvision.transforms.functional.to_tensor(image)

# pass a list of (potentially different sized) tensors
# to the model, in 0-1 range. The model will take care of
# batching them together and normalizing
output = model([image_tensor])
# output is a list of dict, containing the postprocessed predictions

Classification Models
The following classification models were added:

GoogLeNet (Inception v1)
MobileNet V2
ShuffleNet v2
ResNeXt-50 32x4d and ResNeXt-101 32x8d

Datasets
The following datasets were added:

Caltech101, Caltech256, and CelebA
ImageNet dataset (improving on ImageFolder, provides class-strings)
