# Finding Classification Mistakes with FiftyOne Annotations mistakes create an artificial ceiling on the performance of your models. However, finding these mistakes by hand is at least as arduous as the original annotation work! Enter FiftyOne. In this tutorial, we explore how FiftyOne can be used to help you find mistakes in your classification annotations. To detect mistakes in detection datasets, check out [this tutorial](https://voxel51.com/docs/fiftyone/tutorials/detection_mistakes.html). We’ll cover the following concepts: - Loading your existing dataset [into FiftyOne](https://voxel51.com/docs/fiftyone/user_guide/import_datasets.html) - [Adding model predictions](https://voxel51.com/docs/fiftyone/recipes/adding_classifications.html) to your dataset - Computing insights into your dataset relating to [possible label mistakes](https://voxel51.com/docs/fiftyone/user_guide/brain.html#label-mistakes) - Visualizing mistakes in the [FiftyOne App](https://voxel51.com/docs/fiftyone/user_guide/app.html) **So, what’s the takeaway?** FiftyOne can help you find and correct label mistakes in your datasets, enabling you to curate higher quality datasets and, ultimately, train better models! ## Setup If you haven’t already, install FiftyOne: We’ll also need `torch` and `torchvision` installed: In this tutorial, we’ll use a pretrained CIFAR-10 PyTorch model (a ResNet-50) from the web: ## Manipulating the data For this walkthrough, we will artificially perturb an existing dataset with mistakes on the labels. Of course, in your normal workflow, you would not add labeling mistakes; this is only for the sake of the walkthrough. The code block below loads the test split of the [CIFAR-10 dataset](https://voxel51.com/docs/fiftyone/user_guide/dataset_zoo/datasets.html#cifar-10) into FiftyOne and randomly breaks 10% (1000 samples) of the labels: Let’s print some information about the dataset to verify the operation that we performed: ## Add predictions to the dataset Using an off-the-shelf model, let’s now add predictions to the dataset, which are necessary for us to deduce some understanding of the possible label mistakes. The code block below adds model predictions to another randomly chosen 10% (1000 samples) of the dataset: Let’s print some information about the predictions that were generated and how many of them correspond to samples whose ground truth labels were corrupted: ## Find the mistakes Now we can run a method from FiftyOne that estimates the mistakenness of the ground samples for which we generated predictions: The above method added `mistakenness` field to all samples for which we added predictions. We can easily sort by likelihood of mistakenness from code: Let’s open the [FiftyOne App](https://voxel51.com/docs/fiftyone/user_guide/app.html) to visually inspect the results: In a real world scenario, we would then take the ground truth classifications that are likely mistakes and send them off to our annotation provider of choice as annotations to be reviewed. FiftyOne currently offers integrations for both [Labelbox](https://voxel51.com/docs/fiftyone/api/fiftyone.utils.labelbox.html) and [Scale](https://voxel51.com/docs/fiftyone/api/fiftyone.utils.scale.html).