
Welcome to DataTorch 0.4.1! This release improves the annotation import process with a streamlined drag-and-drop experience for COCO imports and official support for Pixel Mask annotations.

## Drag and Drop COCO Import

Importing COCO annotations has just got a lot simpler. We've updated the import modal to support **Drag and Drop**, allowing you to directly upload your COCO JSON files right from the browser.

![Drag and Drop COCO Import](/next/blog/datatorch-0-4-1-coco-drag-drop.png)

Simply open the import dialog, select "COCO" as the format, and drag your file into the release zone. The importer will automatically validate your COCO file structure to ensure compatibility before processing, giving you immediate feedback if there are any formatting issues.

This eliminates the need to use the CLI or Python SDK for quick imports, streamlining your workflow when moving data into DataTorch.

## Pixel Mask Import Support

We have also added support for **Pixel Mask** annotations. You can now import pixel-perfect segmentation masks directly into your projects. This feature is perfect for users working with **binary masks**, **semantic segmentation**, or simply a **folder of images with masks**.

![Pixel Mask Import](/next/blog/datatorch-0-4-1-pixel-mask-import.png)

Whether you are working with complex instance masks or standard binary masks, DataTorch now handles these data types natively. You can select "Pixel Mask" from the import dropdown to view specific instructions for importing your mask data, ensuring that your rigorous segmentation tasks are fully supported.

## Python SDK Updates

Along with these UI improvements, our Python SDK has been updated to support these new import workflows. You can programmatically upload pixel masks and COCO annotations using the latest version of the `datatorch` Python package.

Check out the updated documentation for examples on how to integrate these imports into your automated pipelines.


Drag and drop COCO imports and support for Pixel Mask annotations.

DataTorch Patch 0.4.1 Notes


Welcome to DataTorch 0.4.0, which comes with release of the Pipelines Tab in the annotator, allowing you to see real-time updates on the status of your pipelines run without needing to leave the annotator page.

We've also made some updates to the Python client, adding support for Python 3.12, as well as improvements to allow for smoother installation and programmatic uploading of files.

## Pipelines Tab

![Annotator with Pipelines Tab open](/next/blog/datatorch-0-4-0-annotator.png)

Power users of DataTorch have written pipelines with annotator triggers primarily to run their own models directly on the file being viewed in the browser. However, getting feedback on these pipelines has been a hassle, as users would have to go back into the Pipelines section of their project to see the logs of their actions, breaking workflow and adding complication.

To fix this, we are introducing the Pipelines Tab, a tab directly in the annotator that provides a real time update of each job that is being run in your project, showing the status and logs of each job and step.

![Pipelines Tab close up](/next/blog/datatorch-0-4-0-tab.png)

Now, users can enjoy real time feedback on the progress of their pipeine as soon as they press the trigger in the annotator, and resolve or debug issues in a far more streamlined manner than before. Give it a try with your next annotatorButton triggered pipeline!

## Python Client

We've updated our Python client to support Python version 3.12 and dropped support for Python 3.8. Furthermore, we have expanded the upload functions in the API and CLI tool to make it easier to upload directly to your project programmatically.

Some key changes include:

- Introduction of the upload_to_filesource function to the API, meant to replace the upload_to_default_filesource function, allowing you to specify which storage and dataset you wish to upload files into:

```python
def upload_to_filesource(
        self,
        project: Project,
        file: IO,
        storageId: str = None,
        storageFolderName:str = None,
        dataset: Dataset = None,
        **kwargs
    )
```

- Introduction of the upload option to the CLI, a wrapper around upload_to_filesource, letting you upload files and folders directly via CLI commands
- Example script in the Python repository (in the Examples folder) that demonstrates use of the upload_to_filesource function in a script:

```python
import os
import datatorch as dt

api = dt.api.ApiClient("your-api-key")
proj = api.project("user-name/project-name")
dset = proj.dataset("data-set-name")

folder_to_upload = "uploadme"
upload_to_storage_id = "your-storage-id"

# Get all the file names in the folder
files = [
    f
    for f in os.listdir(folder_to_upload)
    if os.path.isfile(os.path.join(folder_to_upload, f))
]

# Upload files to the selected storage and dataset using their IDs
try:
    for file_name in files:
        file_path = os.path.join(folder_to_upload, file_name)
        with open(file_path, "rb") as file:
            api.upload_to_filesource(
                project=proj,
                file=file,
                storageId=upload_to_storage_id,
                storageFolderName=None,
                dataset=dset,
            )
except Exception as e:
    print(f"Error Uploading: {e}")
```

## Bugfixes & QOL Changes

- Fixed typos on landing page
- Added better feedback for running pipelines in the annotator
- Added handling of uploading files of same name to cloud storages


The Pipelines tab in the annotator has arrived.

DataTorch Patch 0.4.0 Notes


We are thrilled to start the year of 2025 with the launch of our new landing page and blog!

We are thrilled to start the year of 2025 with the launch of our new landing page and blog.

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