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Downloads pretrained Microsoft Vision models

Project description

Microsoft Vision


pip install microsoftvision


Input images should be in BGR format 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].

Example script:

import microsoftvision
import torch

# This will load pretrained model
model = microsoftvision.models.resnext101_32x8d(pretrained=True)

# Load model to CPU memory, interface is the same as torchvision
model = microsoftvision.resnet50(map_location=torch.device('cpu')) 

Example of creating image embeddings:

import microsoftvision
from torchvision import transforms
import torch
from PIL import Image

def get_image():
    img = cv2.imread('example.jpg', cv2.IMREAD_COLOR)
    img = cv2.resize(img, (256, 256))
    img = img[16:256-16, 16:256-16]
    preprocess = transforms.Compose([
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    return preprocess(image).unsqueeze(0) # Unsqueeze only required when there's 1 image in images batch

model = microsoftvision.models.resnet50(pretrained=True)
features = model(get_image())

Should output

torch.Size([1, 2048])

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