choice ( animal_idxs, len ( animal_idxs ) // 6, replace = False ) test_idxs = np. targets, animal_classes )) # Only work with small subset of each dataset to speedup tutorial train_idxs = np. ![]() CIFAR10 ( root = './data', train = False, download = True, transform = transform_normalize ) # Define in (animal) vs out (non-animal) of distribution labels animal_classes = # labels correspond to animal images non_animal_classes = # labels that correspond to non-animal images # Remove non-animal images from the training dataset animal_idxs = np. CIFAR10 ( root = './data', train = True, download = True, transform = transform_normalize ) test_data = torchvision. # Load cifar10 images into tensors for training (rescales pixel values to interval): transform_normalize = torchvision. Use cleanlab to find out-of-distribution examples in the dataset based on the probabilistic predictions of this classifier, as an alternative to relying on feature embeddings. Use cleanlab to find naturally occurring outlier examples in the train_data (i.e. atypical images).įind outlier examples in the test_data that do not stem from training data distribution (including out-of-distribution non-animal images).Įxplore threshold selection for determining which images are outliers vs not.ĭetect outliers using pred_probs from a trained classifierĪdapt our timm network into a classifier by training an additional output layer using the (in-distribution) training data. Use a pretrained neural network model from timm to extract feature embeddings of each image. Pre-process cifar10 into Pytorch datasets where train_data only contains images of animals and test_data contains images from all classes. Overview of what we’ll do in this tutorial: You can easily replace the image dataset + neural network used here with any other Pytorch dataset + neural network (e.g. to instead detect outliers in text data with minimal code changes). This quickstart tutorial shows how to detect outliers (out-of-distribution examples) in image data, using the cifar10 dataset as an example. For the boxplot below, the main title and x-axis label were added.Parts of this site uses JavaScript, but your browser does not support it.ĭetect Outliers with Cleanlab and PyTorch Image Models (timm) # STEP 6: Make sure to always label your graphics. In this particular example, 68 is considered an outlier. Any points that fall below the lower outlier threshold should be considered an outlier. NOTE: The red dashed line represents the lower outlier threshold and for demonstration purposes only. ![]() STEP 5: Using the calculated information from above, construct a boxplot as shown below. STEP 4: Determine Upper Outlier Threshold & Lower Outlier Thresholds: IQR = 3 rd Quartile – 1 st Quartile = 88 – 82 = 6 STEP 3: Determine the Interquartile Range: Use the following steps to construct a boxplot: In addition, if outliers are present, boxplots will typically show them.Īs the example below shows: the smallest non-outlier value and the largest non-outlier value are solid vertical lines connectd to the box by dotted horizontal lines the 1st and 3rd quartiles make up the edges of the box the median is a vertical line somewhere inside the box and the outliers are represented as dots.Ĭonstruct a modified boxplot using the following dataset of student algebra scores:ĩ5, 79, 68, 93, 86, 87, 83, 84, 85, 88, 82, 90, 80, 86, 84 ![]() ![]() The boxplot is based on the Five Number Summary: minimum, 1st quartile, median, third quartile, and maximum. A boxplot is a way to graphically represent quantitative data.
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