|
59 | 59 | "| 6 | Blurry | Images that are blurry or out of focus | blurry |\n", |
60 | 60 | "| 7 | Grayscale | Images that are grayscale (lacking color) | grayscale |\n", |
61 | 61 | "| 8 | Low Information | Images that lack much information (e.g. a completely black image with a few white dots) | low_information |\n", |
| 62 | + "| 9 | Odd Size | Images that are abnormally large or small compared to the rest of the dataset | odd_size |\n", |
62 | 63 | "\n", |
63 | 64 | "\n", |
64 | 65 | "The **Issue Key** column specifies the name for each type of issue in CleanVision code. See our examples which use these keys to detect only particular issue types and specify nondefault parameter settings to use when checking for certain issues." |
|
150 | 151 | "cell_type": "markdown", |
151 | 152 | "metadata": {}, |
152 | 153 | "source": [ |
153 | | - "The main way to interface with your data is via the [Imagelab](https://cleanvision.readthedocs.io/en/latest/cleanvision/imagelab.html#cleanvision.imagelab.Imagelab) class. This class can be used to understand the issues in your dataset at a high level (global overview) and low level (issues and quality scores for each image) as well as additional information about the dataset. It has three main attributes:\n", |
| 154 | + "The main way to interface with your data is via the [Imagelab](../cleanvision/imagelab.rst#cleanvision.imagelab.Imagelab) class. This class can be used to understand the issues in your dataset at a high level (global overview) and low level (issues and quality scores for each image) as well as additional information about the dataset. It has three main attributes:\n", |
154 | 155 | "\n", |
155 | 156 | "- `Imagelab.issue_summary`\n", |
156 | 157 | "- `Imagelab.issues`\n", |
|
645 | 646 | "cell_type": "markdown", |
646 | 647 | "metadata": {}, |
647 | 648 | "source": [ |
648 | | - "You can also create a custom issue type by extending the base class `IssueManager`. CleanVision can then detect your custom issue along with other pre-defined issues in any image dataset! Here's an example of a custom issue manager, which can also be found in the [examples/](https://github.com/cleanlab/cleanvision/blob/main/examples/custom_issue_manager.py) folder of the source code." |
| 649 | + "You can also create a custom issue type by extending the base class [IssueManager](../cleanvision/utils/base_issue_manager.rst#cleanvision.utils.base_issue_manager.IssueManager). CleanVision can then detect your custom issue along with other pre-defined issues in any image dataset! Here's an example of a custom issue manager, which can also be found [here](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/custom_issue_manager.py)" |
649 | 650 | ] |
650 | 651 | }, |
651 | 652 | { |
|
659 | 660 | "import numpy as np\n", |
660 | 661 | "import pandas as pd\n", |
661 | 662 | "from PIL import Image\n", |
662 | | - "from tqdm import tqdm\n", |
| 663 | + "from tqdm.auto import tqdm\n", |
663 | 664 | "\n", |
664 | 665 | "from cleanvision.dataset.base_dataset import Dataset\n", |
665 | 666 | "from cleanvision.issue_managers import register_issue_manager\n", |
|
778 | 779 | { |
779 | 780 | "cell_type": "code", |
780 | 781 | "execution_count": null, |
781 | | - "metadata": {}, |
| 782 | + "metadata": { |
| 783 | + "tags": [] |
| 784 | + }, |
782 | 785 | "outputs": [], |
783 | 786 | "source": [ |
784 | 787 | "issue_types = {issue_name: {}}\n", |
785 | | - "imagelab.find_issues(issue_types)\n", |
| 788 | + "imagelab.find_issues(issue_types)" |
| 789 | + ] |
| 790 | + }, |
| 791 | + { |
| 792 | + "cell_type": "code", |
| 793 | + "execution_count": null, |
| 794 | + "metadata": {}, |
| 795 | + "outputs": [], |
| 796 | + "source": [ |
786 | 797 | "imagelab.report()" |
787 | 798 | ] |
788 | 799 | }, |
|
791 | 802 | "cell_type": "markdown", |
792 | 803 | "metadata": {}, |
793 | 804 | "source": [ |
794 | | - "Beyond the collection of image files demonstrated here, you can alternatively run CleanVision on: [Hugging Face datasets](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/huggingface_dataset.ipynb), [torchvision datasets](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/torchvision_dataset.ipynb), as well as [files in cloud storage buckets like S3, GCS, or Azure](https://github.com/cleanlab/cleanvision-examples/blob/main/cloud_dataset.ipynb)." |
| 805 | + "Beyond the collection of image files demonstrated here, you can alternatively run CleanVision on: [Hugging Face datasets](huggingface_dataset.ipynb), [torchvision datasets](torchvision_dataset.ipynb), as well as [files in cloud storage buckets like S3, GCS, or Azure](https://github.com/cleanlab/cleanvision-examples/blob/main/cloud_dataset.ipynb)." |
795 | 806 | ] |
796 | 807 | } |
797 | 808 | ], |
|
811 | 822 | "name": "python", |
812 | 823 | "nbconvert_exporter": "python", |
813 | 824 | "pygments_lexer": "ipython3", |
814 | | - "version": "3.11.2" |
| 825 | + "version": "3.11.7" |
815 | 826 | } |
816 | 827 | }, |
817 | 828 | "nbformat": 4, |
|
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