Pros of labelme

• More actively maintained with frequent updates and bug fixes
• Larger community and user base, leading to better support and documentation
• Supports a wider range of annotation types, including polygons, rectangles, and points

Cons of labelme

• Slightly more complex interface, which may be overwhelming for new users
• Requires more setup and dependencies compared to the simpler labelmeai version
• May have slower performance on large datasets due to additional features

Code Comparison

labelme:

from labelme import utils
img = utils.img_data_to_arr(img_data)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
    label_name = shape['label']
    if label_name in label_name_to_value:
        label_value = label_name_to_value[label_name]
    else:
        label_value = len(label_name_to_value)
        label_name_to_value[label_name] = label_value

labelmeai:

from labelme import utils
img = utils.img_b64_to_arr(img_b64)
label_name_to_value = {'_background_': 0}
for shape in data['shapes']:
    label_name = shape['label']
    if label_name not in label_name_to_value:
        label_name_to_value[label_name] = len(label_name_to_value)

Pros of labelImg

• Simpler interface focused specifically on bounding box annotations
• Faster performance for basic image labeling tasks
• Supports PascalVOC format natively

Cons of labelImg

• Limited annotation types (mainly bounding boxes)
• Less extensible for custom labeling workflows
• Fewer built-in image processing features

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')
            return False
    return self.saveFileAs()

labelme:

def save_labels(self, filename):
    lf = LabelFile()
    def format_shape(s):
        return dict(label=s.label.encode('utf-8') if PY2 else s.label,
                    line_color=s.line_color.getRgb(),
                    fill_color=s.fill_color.getRgb(),
                    points=[(p.x(), p.y()) for p in s.points],
                    shape_type=s.shape_type)

    shapes = [format_shape(shape) for shape in self.canvas.shapes]
    try:
        lf.save_shapes(filename, shapes, self.imagePath, self.imageData,
                       self.lineColor.getRgb(), self.fillColor.getRgb())
        self.labelFile = lf
        self.filename = filename
        return True
    except LabelFileError as e:
        self.errorMessage(u'Error saving label data', u'<b>%s</b>' % e)
        return False

Pros of Label Studio

• More comprehensive and feature-rich, supporting a wider range of data types and annotation tasks
• Offers a web-based interface, making it easier to collaborate and manage large-scale annotation projects
• Provides integration with popular machine learning frameworks and cloud storage services

Cons of Label Studio

• More complex setup and configuration compared to LabelMe
• Requires more system resources due to its extensive features
• Steeper learning curve for new users

Code Comparison

LabelMe:

from labelme import utils
img = utils.img_b64_to_arr(json_data['imageData'])
label_name_to_value = {'_background_': 0}
for shape in sorted(json_data['shapes'], key=lambda x: x['label']):
    label_name = shape['label']
    if label_name in label_name_to_value:
        label_value = label_name_to_value[label_name]
    else:
        label_value = len(label_name_to_value)
        label_name_to_value[label_name] = label_value

Label Studio:

import label_studio_sdk
ls = label_studio_sdk.Client(url='http://localhost:8080', api_key='your-api-key')
project = ls.start_project(
    title='Image Classification',
    label_config='<View><Image name="image" value="$image"/><Choices name="label" toName="image"><Choice value="Cat"/><Choice value="Dog"/></Choices></View>'
)
ls.upload_data(project.id, [{'image': 'https://example.com/image1.jpg'}])

Pros of CVAT

• More comprehensive and feature-rich annotation tool
• Supports a wider range of annotation tasks and formats
• Better suited for large-scale, collaborative projects

Cons of CVAT

• More complex setup and deployment process
• Steeper learning curve for new users
• Requires more system resources to run effectively

Code Comparison

LabelMe:

from labelme import utils
img = utils.img_b64_to_arr(json_data['imageData'])
labels, shapes = utils.labelme_shapes_to_label(img.shape, json_data['shapes'])

CVAT:

from cvat_sdk import make_client
client = make_client('https://your-cvat-server.com')
client.tasks.create_from_data(name='New Task', labels=[{'name': 'car'}, {'name': 'person'}])

Summary

CVAT offers a more powerful and versatile annotation platform suitable for complex, large-scale projects, while LabelMe provides a simpler, more lightweight solution for basic image annotation tasks. CVAT's advanced features come at the cost of increased complexity and resource requirements, whereas LabelMe is easier to set up and use but has limited functionality compared to CVAT.

Pros of VoTT

• More comprehensive and feature-rich UI for annotation tasks
• Supports a wider range of annotation types, including bounding boxes, polygons, and tags
• Better integration with cloud storage services and export options

Cons of VoTT

• Steeper learning curve due to more complex interface
• Heavier resource usage, which may impact performance on lower-end machines
• Less frequent updates and maintenance compared to LabelMe

Code Comparison

LabelMe:

from labelme import utils
img = utils.img_data_to_arr(img_data)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
    label_name = shape['label']
    if label_name in label_name_to_value:
        label_value = label_name_to_value[label_name]
    else:
        label_value = len(label_name_to_value)
        label_name_to_value[label_name] = label_value

VoTT:

export interface IAsset {
    id: string;
    format: string;
    name: string;
    path: string;
    size: ISize;
    state: number;
    type: AssetType;
}

export interface IProject {
    id: string;
    name: string;
    description?: string;
    tags: ITag[];
    sourceConnection: IConnection;
    targetConnection: IConnection;
    exportFormat: IExportFormat;
    assets?: { [index: string]: IAsset };
}