label-studio

Pros of label-studio

• Comprehensive data labeling platform with support for various data types
• Active development and regular updates
• Extensive documentation and community support

Cons of label-studio

• Larger codebase, potentially more complex to set up and maintain
• May have more features than needed for simple labeling tasks

Code comparison

label-studio:

from label_studio_sdk import Client

ls = Client(url='http://localhost:8080', api_key='your-api-key')
project = ls.start_project(
    title='My Project',
    label_config='<View><Text name="text" value="$text"/></View>'
)

As both repositories refer to the same project, there is no code comparison to be made. The code snippet above demonstrates how to use the label-studio SDK to create a new project.

Summary

label-studio is a comprehensive data labeling platform that supports various data types and offers extensive documentation. It's actively developed and has strong community support. However, its larger codebase may be more complex to set up and maintain, and it might offer more features than necessary for simple labeling tasks. The provided code example shows how to create a new project using the label-studio SDK.

Pros of VoTT

• Specialized for video and image annotation tasks
• Offers offline functionality, allowing work without an internet connection
• Provides a more streamlined interface for specific annotation types

Cons of VoTT

• Limited to visual data annotation, lacking support for text or audio
• Less flexible configuration options compared to Label Studio
• Fewer export formats and integration options

Code Comparison

VoTT (TypeScript):

export interface ITag {
    name: string;
    color: string;
}

export interface IRegion {
    id: string;
    type: RegionType;
    tags: string[];
    boundingBox: IBoundingBox;
}

Label Studio (Python):

class Label(models.Model):
    created_at = models.DateTimeField(_('created at'), auto_now_add=True)
    updated_at = models.DateTimeField(_('updated at'), auto_now=True)
    project = models.ForeignKey('projects.Project', related_name='labels', on_delete=models.CASCADE)
    organization = models.ForeignKey('organizations.Organization', on_delete=models.CASCADE)

Both repositories offer data annotation tools, but they cater to different use cases. VoTT focuses on visual data annotation with a streamlined interface and offline capabilities. Label Studio provides a more versatile platform supporting various data types and offering extensive configuration options. The code snippets highlight the different approaches: VoTT uses TypeScript and focuses on defining interfaces for tags and regions, while Label Studio uses Python with Django models for a more comprehensive data structure.

Pros of CVAT

• More advanced annotation tools for complex tasks like video segmentation
• Supports a wider range of annotation types, including 3D point cloud annotations
• Better suited for large-scale, enterprise-level annotation projects

Cons of CVAT

• Steeper learning curve and more complex setup process
• Less intuitive user interface for beginners
• Requires more system resources to run effectively

Code Comparison

Label Studio configuration example:

<View>
  <Image name="image" value="$image"/>
  <RectangleLabels name="label" toName="image">
    <Label value="Car"/>
    <Label value="Pedestrian"/>
  </RectangleLabels>
</View>

CVAT task creation example:

task = Task.objects.create(
    name="Vehicle Detection",
    overlap=0,
    segment_size=100,
    project=project,
    owner=user,
    assignee=user
)

Both projects are open-source data annotation tools, but they cater to different use cases. Label Studio is more user-friendly and suitable for a wide range of annotation tasks, while CVAT offers more advanced features for complex annotation projects, particularly in computer vision domains.

Pros of labelme

• Lightweight and easy to install with minimal dependencies
• Supports polygon, rectangle, circle, line, and point annotations
• Allows for custom label categories and attributes

Cons of labelme

• Limited support for complex annotation tasks and workflows
• Less extensive documentation and community support
• Fewer built-in integrations with machine learning frameworks

Code Comparison

labelme:

from labelme import utils
img = utils.img_data_to_arr(img_data)
label_name_to_value = {'_background_': 0, 'person': 1, 'car': 2}
lbl = utils.shapes_to_label(img.shape, shapes, label_name_to_value)

Label Studio:

from label_studio_sdk import Client
ls = Client(url='http://localhost:8080', api_key='your-api-key')
project = ls.start_project(title='Image Classification')
ls.upload_data('image.jpg', 'Image')
label_config = '<View><Image name="image" value="$image"/><Choices name="choice" toName="image"><Choice value="cat"/><Choice value="dog"/></Choices></View>'

Label Studio offers a more comprehensive solution with a wider range of annotation types, project management features, and integration capabilities. It's better suited for large-scale, collaborative projects. labelme, on the other hand, is simpler and more lightweight, making it a good choice for smaller projects or quick annotations. The code comparison shows that labelme focuses on image processing and shape annotations, while Label Studio provides a higher-level API for project management and data labeling.

Pros of labelImg

• Lightweight and focused specifically on image annotation
• Simple interface for quick labeling of rectangular bounding boxes
• Supports multiple image formats (JPEG, PNG, BMP, etc.)

Cons of labelImg

• Limited to bounding box annotations only
• Lacks advanced project management and collaboration features
• No built-in support for machine learning model integration

Code Comparison

labelImg:

def saveFile(self, _value=False):
    if self.filePath:
        try:
            self.saveLabels(self.filePath)
            self.setClean()
            return True
        except:
            self.errorMessage(u'Error saving file', u'Error saving labels to file')
            return False

label-studio:

def save_annotation(self, annotation):
    self.annotations.append(annotation)
    self.save()
    return {'id': annotation['id']}

Summary

labelImg is a lightweight tool focused on bounding box annotations for images, while label-studio is a more comprehensive platform supporting various data types and annotation tasks. labelImg offers simplicity and ease of use for specific image labeling needs, but lacks the advanced features and flexibility provided by label-studio's broader ecosystem.

Pros of doccano

• Lightweight and simpler to set up and use
• Supports a wider range of annotation types out-of-the-box
• More focused on text annotation tasks

Cons of doccano

• Less extensive customization options
• Fewer integrations with external tools and services
• Limited support for image and audio annotation

Code Comparison

doccano:

from doccano_api_client import DoccanoClient

client = DoccanoClient('http://localhost:8000')
client.login(username='admin', password='password')
project = client.get_project(project_id=1)

Label Studio:

from label_studio_sdk import Client

ls = Client(url='http://localhost:8080', api_key='your-api-key')
project = ls.get_project(1)
tasks = project.get_tasks()

Both repositories provide Python clients for interacting with their respective APIs. doccano's client is more straightforward, while Label Studio's SDK offers more extensive functionality and integration options.