SqueezeNet
import torch model = torch.hub.load('pytorch/vision:v0.10.0', 'squeezenet1_0', pretrained=True) # or # model = torch.hub.load('pytorch/vision:v0.10.0', 'squeezenet1_1', 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 H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and 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/images/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[0]) # The output has unnormalized scores. To get probabilities, you can run a softmax on it. probabilities = torch.nn.functional.softmax(output[0], dim=0) print(probabilities) # Download ImageNet labels !wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt # Read the categories with open("imagenet_classes.txt", "r") as f: categories = [s.strip() for s in f.readlines()] # Show top categories per image top5_prob, top5_catid = torch.topk(probabilities, 5) for i in range(top5_prob.size(0)): print(categories[top5_catid[i]], top5_prob[i].item()) Model Description
Model squeezenet1_0 is from the SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size paper
Model squeezenet1_1 is from the official squeezenet repo. It has 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.
Their 1-crop error rates on ImageNet dataset with pretrained models are listed below.
| Model structure | Top-1 error | Top-5 error |
|---|---|---|
| squeezenet1_0 | 41.90 | 19.58 |
| squeezenet1_1 | 41.81 | 19.38 |
References
