import torch # get list of models torch.hub.list('zhanghang1989/ResNeSt', force_reload=True) # load pretrained models, using ResNeSt-50 as an example model = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True) model.eval()
All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape
(3 x H x W), where
W are expected to be at least
The images have to be loaded in to a range of
[0, 1] and then normalized using
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225].
Here’s a sample execution.
# Download an example image from the pytorch website import urllib url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg") try: urllib.URLopener().retrieve(url, filename) except: urllib.request.urlretrieve(url, filename)
# sample execution (requires torchvision) from PIL import Image from torchvision import transforms input_image = Image.open(filename) preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model # move the input and model to GPU for speed if available if torch.cuda.is_available(): input_batch = input_batch.to('cuda') model.to('cuda') with torch.no_grad(): output = model(input_batch) # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes print(output) # The output has unnormalized scores. To get probabilities, you can run a softmax on it. print(torch.nn.functional.softmax(output, dim=0))
ResNeSt models are from the ResNeSt: Split-Attention Networks paper.
While image classification models have recently continued to advance, most downstream applications such as object detection and semantic segmentation still employ ResNet variants as the backbone network due to their simple and modular structure. We present a simple and modular Split-Attention block that enables attention across feature-map groups. By stacking these Split-Attention blocks ResNet-style, we obtain a new ResNet variant which we call ResNeSt. Our network preserves the overall ResNet structure to be used in downstream tasks straightforwardly without introducing additional computational costs. ResNeSt models outperform other networks with similar model complexities, and also help downstream tasks including object detection, instance segmentation and semantic segmentation.