---
title: "Item Actions"
slug: "manage-your-data"
tags: ["Assign items to test subsets", "Assign items to train subsets", "Classify items", "Clone items", "Collections", "Create a task", "Datasets to an existing task", "Delete annotations from items", "Delete dataset items", "Deployment slot", "Download annotations", "Download files", "Export datasets in COCO", "Export datasets in VOC formats", "Export datasets in YOLO", "Export items as JSON file", "Extract Embeddings", "File actions", "Find similar items", "Items to validation subsets", "Labeling tasks", "Move items to a folder", "Open item in the studio", "Open items in a new tab", "Open with", "Predictions by a trained model", "Predictions with a model", "Remove items from subsets", "Rename items", "Run items with application", "Run items with Pipelines", "Similarity", "Split items into ML subsets"]
updated: 2026-04-27T06:14:53Z
published: 2026-04-27T06:14:53Z
canonical: "docs.dataloop.ai/manage-your-data"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dataloop.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Item Actions

The Dataset Browser lets you take action at both the dataset and item levels. To get started, select the scope of items you want to work with either the entire dataset or specific items. Then, click ***Actions*** to access the available options. Some actions may be unavailable when multiple items are selected.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/image(691).png)

***Right-Click***: Right-clicking on an item opens the actions menu. While the menu appears on the item you clicked, the actions apply to all currently selected items. The same set of options is also available under Dataset Actions.

***Multi-Select Items:*** To select multiple items, hold **Ctrl** (or Cmd on Mac) and click the items you want to include.

---

## Item Details

The Item Details panel displays details of the selected item, including general details, tasks and annotations, automation, and metadata. [Learn more](https://docs.dataloop.ai/docs/data-browser-overview#items-preview)

---

## 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.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/image(692).png)

### Open Items in a New 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. **Right-Click**, or click **Actions**.
3. Select **File Actions** > **Open File in New Tab**. The selected file will be opened in a new browser tab.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/db_new_tab.png)

### Open Item in the 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. **Right-Click**, or click **Actions**.
3. Select **File Actions** > **Open File in Studio**. The selected file will be opened in the default annotation studio.

### Export as JSON File

1. In the Dataset Browser, select the item you want to export.
2. **Right-Click**, or click **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.

### Download Annotations

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

### Rename Items

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

> [!WARNING]
> File Moving: External Storage
> 
> Moving items within a dataset that resides on **external (read-only) storage** is **not possible**. This is a **designed limitation**, since Dataloop cannot physically move files in your storage without causing mismatches between the dataset and the actual storage structure.

### Classify Items

1. In the Dataset Browser, select the item you want to classify.
2. **Right-Click**, or click **Actions**.
3. Select **File Actions** > **Classification** from the list. [Learn more](https://docs.dataloop.ai/docs/classify-item) about the classification.

### Clone Items

1. In the Dataset Browser, select the item you want to clone.
2. **Right-Click**, or click **Actions**.
3. Select **File Actions** > **Clone**. [Learn more](https://docs.dataloop.ai/docs/data-versioning#clone-dataset-or-items) about the cloning process.

### Download Files

Download up to 100 files

- You can download up to 100 files per selection. To download more, use the SDK.
- Only **Developer** or **Owner** can download files.

1. In the Dataset Browser, select the item(s) you want to export.
2. **Right-Click**, or click **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

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

### Delete Items

1. In the Dataset Browser, select the item you want to delete.
2. **Right-Click**, or click **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. **Right-Click**, or click **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 Tasks

1. In the Dataset Browser, Click **Actions.**
2. Select **Labeling Tasks** -> [Create New Task](/1-3-8/docs/create-labeling-tasks#🔖-create-a-labeling-task) from the list.

Items to Task or Model

When creating a task or model from the Dataset browser, it includes all items in the dataset.

### Add Items to Tasks

Add items in the datasets to an existing task.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/db_add_tasks.png)

1. In the Dataset Browser, click **Actions**.
2. Select **Labeling Tasks** -> **Add to an Existing Task** → [Manual Task](/1-3-8/docs/manage-your-tasks#add-items-to-existing-tasks) from the list.

Items to Task or Model

When creating a task or model from the Dataset browser, it includes all items in the dataset.

### Add Items to Pipeline Tasks

Add items in the datasets to an existing pipeline task.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/db_add_pipeline_tasks.png)

1. In the Dataset Browser, click **Actions**.
2. Select **Labeling Tasks** -> **Add to an Existing Task** → [Pipeline Task](/1-3-8/docs/manage-your-tasks#add-items-to-pipeline-tasks) from the list.

---

## Models

### Predict with a Model

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

1. **Open the Dataset Browser**
  - From the Dataloop main interface, click the **Data** menu on the left-hand panel.
  - Choose and open the relevant **dataset** from the list.
2. **Select Items for Prediction**
  - In the Dataset Browser, select the items you want to pre-label (you can select one or multiple items).
  - Right-click on the selected items to open the context menu.
3. **Access the Predict Function**
  - From the context menu, go to **Models → Predict**.
  - The **Predict With Model** panel will appear on the right side of the screen.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/pre-labeling_db.png)

1. **Choose a Model and Run Prediction**
  1. Under the **Deployed** section, choose the model you want to use.
  2. Click **Predict (Items)** to begin pre-labeling the selected items. A confirmation message is displayed as `Running model prediction - follow the activity bell. Open the Service Page`. Once the prediction is complete, the labels will be applied to the items and available for review or post-processing.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/pre-labeling_db_predict_with_model.png)

1. **Deploy a Model for Prediction**
  1. Under the **Undeployed** section, select the model and click **Deploy** before using it for predictions. The **Model Version Deployment** panel is displayed.
  2. Make the service and execution configurations as required, refer to the [Edit Services](/1-3-8/docs/services#edit-service-settings) article for more details.
  3. Once complete, click **Deploy Model**. The deployed model will be added under the Deployed section.
  4. Select the model and click **Predict Items.**

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/pre-labeling_db_predict_with_model_deploy.png)

💡 Tips

- **Install a New Model:** If the required model isn't available, click **Marketplace** to browse and [install new models](/1-3-8/docs/dl-hub-models#model-installations) into your project.

### Extract Embeddings

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.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/db_extract_emb.png)

1. Select a Model from the Deployed section, or click on the Marketplace to install a new model.
  1. If there are no models available, click [Install Model](/1-3-8/docs/dl-hub-models#model-installations).
  2. Once installed a model, click [Deploy](/1-3-8/docs/model-versions#deploy-models)
2. 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](https://docs.dataloop.ai/docs/organize-your-data#split-items-into-subsets) article.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/db_split_subsets.png)

### Assign Items to Test Subsets

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. **Right-Click**, or click **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 Items to Train Subsets

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. **Right-Click**, or click **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 Items to Validation Subsets

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. **Right-Click**, or click **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.

### Remove Items from Subsets

1. In the Dataset Browser, select the item.
2. **Right-Click**, or click **Actions**.
3. Select **Models → Remove from Subsets**.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/db_remove_subsets(1).png)

1. Click **Yes** to confirm it.

---

## 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](https://github.com/dataloop-ai-apps/dtlpy-converters).

- **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](https://github.com/ultralytics/yolov5): 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](https://docs.ultralytics.com/datasets/detect/voc/): 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.

Info

Develop a custom converter and deploy it to a UI-Slot anywhere on the platform, or embed it as a Pipeline node. To learn more, contact Dataloop support.

### Export Datasets in COCO/YOLO/VOC Formats

1. In the Dataset Browser, select the item(s) in the Dataset Browser.
2. **Right-Click**, or click **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.

Important

- Images: The COCO/YOLO/VOC Format conversion supports only images.
- Videos: You can download COCO, YOLO, or VOC formats for video files; however, only the annotations from the first frame are included.

---

## 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](https://docs.dataloop.ai/docs/organize-your-data#add-items-to-a-collection).

---

## 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. **Right-Click**, or click **Actions**.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/dataset_find-similar-items.png)

1. Select **Similarity** -> **Find similar items**
2. Click on the **Feature Set** name. The [Clustering](https://docs.dataloop.ai/docs/clustering) tab is displayed with similar items are selected.

---

## Run Items with Applications

A list of active applications is displayed, and select one of the applications to run the chosen item(s).

1. In the Dataset Browser, select the item.
2. **Right-Click**, or click **Actions**.
3. Select the **Run with FaaS** to select a function to execute with the selected items.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/run_with_faas.png)
4. You can:
  1. Search functions by function name, project name, and service name.
  2. Filter functions by public functions, project functions and all functions in the user’s projects.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/run_with_faas_filter.png)
5. Select a function from the list.
6. Click **Execute**. A confirmation message will be displayed.

Notice

The following messages are displayed when you run the item with an application:

- **Single-item input**: Selecting multiple items will trigger separate executions for each item.
- **List (item[]) input**: Selecting multiple items will trigger a single execution with all items.
- **Over 1000 items (item[] input)**: The function is disabled, and a warning is shown indicating that execution with more than 1000 items is not supported.

---

## Run Items with Pipelines

A list of **active** pipelines containing input items is displayed. Select one of the pipelines to run the chosen item(s). Once selected and executed, the pipeline will be triggered starting from its [start node](https://docs.dataloop.ai/docs/creating-pipelines#create-your-own-pipeline:~:text=Verify%20the%20Pipeline-,Starting%20Node,-The%20starting%20icon).

Notice

The following messages are displayed when you run the item with a pipeline:

- **Single-item input**: Selecting multiple items will trigger separate executions for each item.
- **List (item[]) input**: Selecting multiple items will trigger a single execution with all items.
- **Over 1000 items (item[] input)**: The function is disabled, and a warning is shown indicating that execution with more than 1000 items is not supported.

Learn more about [Pipelines](https://docs.dataloop.ai/docs/pipelines-overview#more-about-the-pipelines).

1. In the Dataset Browser, select the item.
2. **Right-Click**, or click **Actions**.
3. Select the **Run with Pipeline**. It allows you to execute the selected items in the selected pipeline.

![](https://cdn.document360.io/53f32fe9-1937-4652-8526-90c1bc78d3f8/Images/Documentation/run_with_pipeline.png)
4. Search and select a pipeline from the list.
5. Click **Execute**. A confirmation message will be displayed.

To view more actions available on the dataset actions, refer to the [Manage Datasets](https://docs.dataloop.ai/docs/manage-datasets) article.

UI slots create a button in the Dataloop platform, allowing managers to associate to that location FaaS functions, so users can invoke them when needed. Once a Slot is activated, users can execute the function through the UI Slot in the Dataset browser, Task browser, or Annotation studio.