Dataset Actions

Dataset Browser allows you to perform the following actions based on dataset and item level. The following actions are available to perform when you click on the Dataset Actions. A few actions are not applicable if you select more than one item.

Right-Click: You can also use the right-click to perform the following actions. The menu is opened only for the individual item on which the right-click is performed. Once you click on either Dataset Actions or Right-Click on an Item, the following options are listed:

File Actions

You can perform the following action on an item within the Dataset. Please note that actions from the right-click menu cannot apply to multiple selected items simultaneously.

Open an Item in a New Browser Tab

It allows you to view images, play audio files, etc. in a new browser tab.

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select File Actions > Open File in New Tab. The selected file will be opened in a new browser tab.

Open an Item in the Annotation Studio

It allows you to view images, play audio files, etc. in a new browser tab. In the Dataset Browser, identify the item and double-click. The item will be opened in the default annotation studio based on the type of the item, such as image, audio, video, etc. or, you can follow these steps:

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select File Actions > Open File in Studio. The selected file will be opened in the default annotation studio.

Export Items as JSON file

1. In the Dataset Browser, select the item you want to export.
2. Click Dataset Actions.
3. Select File Actions > Export JSON. The selected dataset or items will be exported as a JSON file in a ZIP file and will contain annotation, if available. For example, a JPG image will be downloaded as a JSON file.

Rename an Item

1. In the Dataset Browser, select the item you want to rename.
2. Click Dataset Actions.
3. Select File Actions -> Rename.
4. Edit the name, and click Rename. A confirmation message is displayed.

Move Items to a Folder

1. In the Dataset Browser, select the item you want to move.
2. Click Dataset Actions.
3. Select File Actions > Move to Folder.
4. Select a folder from the list.
5. Click Move. A confirmation message is displayed.

Classify an Item

1. In the Dataset Browser, select the item you want to classify.
2. Click Dataset Actions.
3. Select File Actions > Classification from the list. Learn more about the classification.

Clone an Item

1. In the Dataset Browser, select the item you want to clone.
2. Click Dataset Actions.
3. Select File Actions > Clone. Learn more about the cloning process.

Download Files

1. In the Dataset Browser, select the item(s) you want to export.
2. Click Dataset Actions.
3. Select File Actions > Download Files. The selected item will be downloaded. For example, JPG image will be downloaded as a JPG file.

Delete Annotations from an Item

1. In the Dataset Browser, select the item you want to delete annotations.
2. Click Dataset Actions.
3. Select File Actions > Delete Annotations. A confirmation message is displayed.

Delete Dataset Items

1. In the Dataset Browser, select the item you want to delete.
2. Click Dataset Actions.
3. Select File Actions > Delete Items.
4. Click Yes. A confirmation message is displayed.

Open With

The Open With option allows you to open items in the respective labeling studios.

1. In the Dataset Browser, select the item.
2. Click Dataset Actions -> Open With.
3. Select the Annotation Studio. The type of the annotation studio will display based on the type of the item, such as image, audio, video, etc.

Labeling Tasks

Create a Task

1. In the Dataset Browser, Click Dataset Actions.
2. Select Labeling Tasks -> Create a New Task from the list.

Add a Dataset to an Existing Task

1. In the Dataset Browser, click Dataset Actions.
2. Select Labeling Tasks -> Add to an Existing Task from the list.

Models

Generate Predictions with a Model

You can use the dataset items to generate predictions by using a trained, pre-trained, and deployed model.

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select Models > Predict.
4. Search and select a trained and deployed model from the list.
5. Click Predict. A confirmation message is displayed.

Generate Predictions by Using a Trained Model

You can use only trained and deployed models for generating predictions. To deploy a trained model, perform the following instructions:

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select Models > Predict.
4. Identify the trained model, and click Deploy. The Model Version Deployment page is displayed.
5. In the Deployment and Service Fields tabs, make changes in the available fields as needed.
6. Click Deploy. A confirmation message is displayed.

Assign an Item to a Model's Test Datasets

You can assign the selected items to model's test datasets. When you assign, a tag (Test) will be added to the item details.

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select Models -> Assign to Subset.
4. Select the Test. The Test Dataset is used to evaluate the performance of a trained model on new, unseen data.

Assign an Item to a Model's Train Datasets

You can assign the selected items to model's train datasets. When you assign, a tag (Train) will be added to the item details.

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select Models -> Assign to Subset.
4. Select the Train. The Train Dataset is used to train the machine learning model, helping it learn patterns and make predictions.

Assign an Item to a Model's Validation Datasets

You can assign the selected items to model's Validation datasets. When you assign, a tag (Validation) will be added to the item details.

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select Models -> Assign to Subset.
4. Select the Validation. The Validation Dataset is used to fine-tune the model and optimize its hyperparameters, helping prevent overfitting.

Extract Embeddings from an Item(s)

1. In the Dataset Browser, select the items you want to extract embeddings.
2. Click Dataset Actions and select the Models -> Extract Embeddings option from the list. An Extract Embeddings pop-up is displayed.

3. Select a Model from the Deployed section, or click on the Marketplace to install a new model. If there are no models available, click Install Model.
   1. Once installed a model, click Deploy.
4. Click Embed to initiate the embeddings' extraction. Use the Notifications bell icon to view status.

Split Items into ML Subsets

Refer to the Split Into Subsets article.

Remove an Item from a Model Subset

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select Models.
4. Select the following options as per requirement. A confirmation message will be displayed, and the respective tag will be deleted from the item details.
   1. Remove from Test Set.
   2. Remove from Train Set.
   3. Remove from Validation Set.

Deployment Slot

The Dataset browser incorporates significant automation capabilities, enabling you to export dataset items in industry-standard formats through the following functions. Any function available within this application can be applied to selected items or an active query.

In addition to the Dataloop format, annotations can be exported in industry-standard formats. These are facilitated as functions deployed to the UI Slot of the Dataset-browser.

To learn more about the converters, their input, and output structures, visit their Git repo.

• COCO Converter: This tool is used to convert data annotations from other formats into the COCO (Common Objects in Context) format or vice versa. COCO is a popular dataset format for object detection, segmentation, and image captioning tasks.
• YOLO Converter: YOLO (You Only Look Once) is a popular object detection algorithm. A YOLO Converter is used to convert annotations between YOLO format and other annotation formats, making it easier to work with YOLO-based models and datasets.
• VOC Converter: VOC (Visual Object Classes) is another dataset format commonly used in computer vision tasks. A VOC Converter allows you to convert annotations between VOC format and other formats, facilitating compatibility with different tools and models.

Export Datasets in COCO/YOLO/VOC Formats

1. In the Dataset Browser, select the item(s) in the Dataset Browser.
2. Click Dataset Actions.
3. Select Deployment Slot and select one of the following format:
   1. COCO Converter.
   2. YOLO Converter.
   3. VOC Converter.
4. A message is displayed as the execution of function <global-converter> was created successfully, please check activity bell. A ZIP file will be created and downloaded.

Collections

The Collections feature in Dataloop's Data Browser helps streamline data management by allowing you to tag or group specific sets of items based on task needs (e.g., annotation, review, training).
Selecting this option allows you to add selected items to a collection folder.

Similarity

The Similarity feature in Dataloop enables you to identify items that are similar to a selected item. This helps with data exploration, cleaning, and optimizing ML subset splits. To use this feature, ensure that the selected item has an available feature vector. After selecting the Similarity -> Find similar items, you can view all associated feature set names and the model name that generated the item's vector.

Find Similar Items

1. In the Dataset Browser, select the item to download annotation.
2. Click Dataset Actions.

3. Select Similarity -> Find similar items
4. Click on the Feature Set name. The Clustering tab is displayed with similar items are selected.

Run Items with a FaaS or Pipeline

Run a selected item to a function from a running service (FaaS) or a running pipeline.

1. In the Dataset Browser, select the item.
2. Click Dataset Actions.
3. Select:
   1. Run with FaaS: It allows you to select a function to execute with the selected items.
   2. Run with Pipeline: It allows you to select a pipeline to execute with the selected items.
4. Select a function or pipeline from the list.
5. Click Execute. A confirmation message will be displayed.

Automation Info and Warning Messages

The following information and warning messages are displayed when you run the item with a FaaS, Pipeline, or Model predictions.

• When you select more than one item with a function/pipeline/model with item input: Triggering multiple items to a function with single-item input will execute each item separately, resulting in the creation of multiple executions.
• When you select more than one item to a functions/pipelines with item[] input: Triggering multiple items to a function with an item[] list input will execute all items in a single execution.
• When you select more than 1000 items to a functions/pipelines with item[] input: The functions with the item[] input are disabled, and displays a warning message that the function with the item[] input cannot be executed with more than 1000 items in the list.

Download Annotations

1. In the Dataset Browser, select the item to download annotation.
2. Click Dataset Actions.
3. Select Download Annotations from the list. The annotations of the selected file will be downloaded as a JSON file.

To view more actions available on the dataset actions, refer to the Manage Datasets article.