Pros of labelImg

• Simpler interface, focusing primarily on bounding box annotations
• Faster performance for basic labeling tasks
• Supports YOLO format natively

Cons of labelImg

• Limited annotation types (mainly bounding boxes)
• Less extensive documentation and community support
• Fewer advanced features for complex labeling tasks

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):
    if self.output_dir:
        lblsave(filename, self.canvas.shapes, self.imagePath, self.imageData,
                self.lineColor.getRgb(), self.fillColor.getRgb(),
                self.otherData)
        self.setDirty(False)

labelImg focuses on a straightforward saving process, while labelme incorporates more parameters and flexibility in its save function. This reflects the overall design philosophy of each tool, with labelImg prioritizing simplicity and labelme offering more advanced features and customization options.

Pros of VoTT

• More comprehensive UI with a wider range of annotation tools
• Supports multiple export formats, including COCO, Pascal VOC, and TFRecords
• Integrates with cloud storage services like Azure Blob Storage

Cons of VoTT

• Steeper learning curve due to more complex interface
• Requires more system resources, potentially slower on older machines
• Less focus on simplicity and quick setup compared to labelme

Code Comparison

labelme:

import labelme
from labelme import utils

img = utils.img_data_to_arr(img_data)
labels, shapes, _ = labelme.LabelFile.load_image_shapes(json_file)

VoTT:

import { Env } from "vott-react";
import { AssetService } from "vott-react/lib/services/assetService";

const assetService = new AssetService(Env.getAssetServiceUrl());
const assets = await assetService.getAssets(projectId);

Summary

VoTT offers a more feature-rich annotation tool with cloud integration and multiple export formats, making it suitable for larger projects. However, it may be overkill for simpler tasks. labelme provides a straightforward, lightweight solution for quick image annotation, but lacks some advanced features and export options found in VoTT.

Pros of CVAT

• More comprehensive annotation tool with support for various tasks (object detection, segmentation, classification)
• Collaborative features for team-based annotation projects
• Supports video annotation and tracking

Cons of CVAT

• More complex setup and infrastructure requirements
• Steeper learning curve for new users
• Requires more system resources to run

Code Comparison

CVAT (Python):

from cvat_sdk import make_client
from cvat_sdk.core.proxies.tasks import ResourceType

client = make_client("https://your-cvat-server.com")
client.tasks.create(
    name="My Task",
    labels=[{"name": "car"}, {"name": "person"}],
    resource_type=ResourceType.LOCAL,
    resources=["path/to/image.jpg"],
)

Labelme (Python):

import labelme

labelme.main()

Summary

CVAT offers a more feature-rich and collaborative annotation platform suitable for larger projects and teams, while Labelme provides a simpler, lightweight solution for basic image annotation tasks. CVAT's setup is more complex but offers greater flexibility, whereas Labelme is easier to get started with but has limited functionality compared to CVAT.

Pros of Label Studio

• More comprehensive and feature-rich, supporting a wider range of data types and labeling tasks
• Offers a web-based interface, making it easier to collaborate and manage large-scale projects
• Provides advanced project management features and integrations with popular ML frameworks

Cons of Label Studio

• More complex setup and configuration compared to Labelme's simpler approach
• Steeper learning curve for new users due to its extensive feature set
• Requires more system resources to run, especially for larger projects

Code Comparison

Labelme (Python):

import labelme
from labelme import utils

img = utils.img_data_to_arr(img_data)
labels, shapes, _ = labelme.LabelFile.load_image_shapes(json_file)

Label Studio (JavaScript):

import LabelStudio from 'label-studio';

const labelStudio = new LabelStudio('label-studio', {
  config: `<View><Image name="img" value="$image"/></View>`,
  interfaces: ["panel", "update", "submit"],
  task: { annotations: [], predictions: [], data: { image: "url" } }
});

Both projects serve as annotation tools, but Label Studio offers a more comprehensive solution for diverse labeling needs, while Labelme provides a simpler, lightweight option for basic image annotation tasks.

Pros of doccano

• Supports a wider range of annotation types, including text classification, sequence labeling, and document classification
• Offers a collaborative annotation environment with user management and role-based access control
• Provides a RESTful API for integration with other systems and workflows

Cons of doccano

• Requires more setup and configuration compared to labelme's simpler installation process
• Has a steeper learning curve due to its more complex feature set
• May be overkill for simple image annotation tasks that labelme handles efficiently

Code comparison

labelme:

import labelme
from labelme import utils

img = utils.img_data_to_arr(img_data)
labels, shapes, _ = labelme.LabelFile.load_image_shapes(json_file)

doccano:

from doccano_api_client import DoccanoClient

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