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Online drift detection for Camelyon17 medical imaging dataset

This notebook demonstrates a typical workflow for applying online drift detectors to streams of image data. For those unfamiliar with how the online drift detectors operate in alibi_detect we recommend first checking out the more introductory example Online Drift Detection on the Wine Quality Dataset where online drift detection is performed for the wine quality dataset.

Install the wilds library to fetch the dataset used in the example:

pip install wilds

This notebook requires the wilds, torch and torchivision packages which can be installed via pip:

[ ]:
!pip install wilds torch torchvision
[1]:
from typing import Tuple, Generator, Callable, Optional
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader
import torchvision.transforms as transforms
from wilds.common.data_loaders import get_train_loader
from wilds import get_dataset

torch.manual_seed(0)
np.random.seed(0)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
%matplotlib inline

Data

We will use the Camelyon17 dataset, one of the WILDS datasets of Koh et al, (2020) that represent “in-the-wild” distribution shifts for various data modalities. It contains tissue scans to be classificatied as benign or cancerous. The pre-change distribution corresponds to scans from across three hospitals and the post-change distribution corresponds to scans from a new fourth hospital.