![]() ![]() The images are grayscale, so set channels to 1. You're logging only one image, so batch_size is 1. Therefore, the tensors need to be reshaped. However, tf.summary.image() expects a rank-4 tensor containing (batch_size, height, width, channels). Notice that the shape of each image in the data set is a rank-2 tensor of shape (28, 28), representing the height and the width. Print("Label: ", train_labels, "->", class_names]) To understand how the Image Summary API works, you're now going to simply log the first training image in your training set in TensorBoard.īefore you do that, examine the shape of your training data: print("Shape: ", train_images.shape) # Names of the integer classes, i.e., 0 -> T-short/top, 1 -> Trouser, etc.Ĭlass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', (train_images, train_labels), (test_images, test_labels) = \ # The labels are integers representing classes.įashion_mnist = _mnist The data is already divided into train and test. This dataset consist of 70,000 28x28 grayscale images of fashion products from 10 categories, with 7,000 images per category.įirst, download the data: # Download the data. You're going to construct a simple neural network to classify images in the the Fashion-MNIST dataset. "This notebook requires TensorFlow 2.0 or above." Print("TensorFlow version: ", tf._version_)Īssert version.parse(tf._version_).release >= 2, \ # Load the TensorBoard notebook extension. # %tensorflow_version only exists in Colab. You will work through a simple but real example that uses Image Summaries to help you understand how your model is performing. You will also learn how to take an arbitrary image, convert it to a tensor, and visualize it in TensorBoard. ![]() In this tutorial, you will learn how to use the Image Summary API to visualize tensors as images. You can also log diagnostic data as images that can be helpful in the course of your model development. This can be extremely helpful to sample and examine your input data, or to visualize layer weights and generated tensors. See object tags.Using the TensorFlow Image Summary API, you can easily log tensors and arbitrary images and view them in TensorBoard. Name of the object tag that provided the region to be labeled. Name of the tag used to label the region. Identifier for the specific annotation result for this task. Identifier for a specific project in Label Studio.Īrray containing the labeling results for the task.Īrray containing the results of the labeling or annotation task. Identifier for the labeling task from the dataset.ĭata copied from the input data task format. Review the full list of JSON properties in the API documentation. Supports audio transcription labeling projects that use the Audio tag with the TextArea tag. ASR_MANIFESTĮxport audio transcription labels for automatic speech recognition as the JSON manifest format expected by NVIDIA NeMo models. For more information, see the GitHub repository for the Label Studio Converter tool. If you don’t see a format that works for you, you can contribute one. Label Studio supports many common and standard formats for exporting completed labeling tasks. Using the id from the created snapshot as the export primary key, or export_pk, make a GET request to download the export file.Check the status of the export file created using the id as the export_pk. ![]() The response includes an id for the created file. Make a POST request to create a new export file or snapshot.Export snapshots using the Snapshot APIįor a large labeling project with hundreds of thousands of tasks, do the following: Snapshots include all tasks without annotations by default. For example: curl -X GET If your project is large, you can use a snapshot export (or snapshot SDK) to avoid timeouts in most cases. If you want to easily export all tasks including tasks without annotations, you can call the Easy Export API with query param download_all_tasks=true. Label Studio open source exports tasks with annotations only by default. Export all tasks including tasks without annotations For a small labeling project, call the export endpoint to export annotations. You can call the Label Studio API to export annotations. To enable logs: DEBUG=1 LOG_LEVEL=DEBUG label-studio export -path= Export using the Easy Export API Use the following command to export data and annotations. For more information, see the following section. You can also use a console command to export your project. If the export times out, see how to export snapshots using the SDK or API. If you want to apply tab filters to the export, try to use export snapshots using the SDK or API.Cancelled annotated tasks will be included in the exported result too.The export will always include the annotated tasks, regardless of filters set on the tab. ![]()
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