Tag: image labelling

Introducing PixLab Annotate 1.97: A Game-Changer in Image Annotation

PixLab is thrilled to announce the release of PixLab Annotate 1.97, the latest version of our cutting-edge online image annotation tool. Designed to streamline and enhance the process of image labeling and segmentation, PixLab Annotate 1.97 is packed with new features and improvements that make it an indispensable tool for machine learning and computer vision projects.

Annotate Landing Page

Key Features of PixLab Annotate 1.97

  • Enhanced Labeling Tools: The new version includes advanced rectangle and polygon zoom drag labeling tools, making the process of annotating images more precise and efficient.

  • Optimized for Instance Segmentation: PixLab Annotate 1.97 is optimized for instance segmentation tasks, including support for Mask R-CNN, ensuring compatibility with advanced machine learning models.

  • Client-Side Persistent Storage: With the introduction of client-side persistent storage, your data remains on your device, eliminating the need for data transfer and enhancing security and speed.

  • Full-Screen Display and Snapshot Capture: The full-screen mode and snapshot capture feature allow for a more immersive and detailed annotation experience.

  • Consistent JSON Output: The tool generates consistent JSON output, which is readily accepted by most machine learning frameworks, facilitating seamless integration into your workflow.

  • Improved Label Management: Create, modify, and delete labels with ease, thanks to the improved label management system.

  • Annotation Events Tracking: Keep track of your annotation history with the enhanced tracking feature, which includes undo and redo options for better control over your work.

  • User-Friendly Interface: The interface of PixLab Annotate 1.97 is intuitive and easy to navigate, making it accessible for both beginners and experienced users.

Why Choose PixLab Annotate 1.97?

PixLab Annotate 1.97 is more than just an annotation tool; it's a comprehensive solution designed to meet the needs of professionals in the field of machine learning and computer vision. Whether you're working on a small project or a large-scale enterprise task, PixLab Annotate 1.97 offers the reliability, efficiency, and accuracy you need.

Get Started Today!

Experience the power and convenience of PixLab Annotate 1.97 today. Visit PixLab Annotate to explore these new features and see how they can transform your image annotation tasks. Join the community of professionals who are already leveraging the benefits of PixLab Annotate in their projects.

Stay tuned for more updates and enhancements as we continue to evolve and improve PixLab Annotate to meet your needs.

Happy annotating!

Announcing PixLab Annotate - An Online Image Annotation Tool

The PixLab Computer Vision team is pleased to announce the immediate availability of PixLab Annotate. A web based image annotation, labeling & segmentation tool for Machine Learning model training tasks and beyond...

Annotate Features Set:

  • Rectangle, Polygon, Zoom & Drag labeling tool.
  • Consistent JSON output accepted by most Machine Learning frameworks.
  • Optimized for instance segmentation (Mask R-CNN, etc).
  • Client-side persistent storage - No data transfer involved.
  • Persistent & easy label management (Create, Modify & Delete).
  • Full screen display & Snapshot capture.

Straightforward image segmentation and labeling thanks to the Rectangle & Polygon built-in tool!

At PixLab, we really believe that annotate is a great fit for data scientists, developers or students looking for a straightforward, online image segmentation and labeling tool for their daily machine learning model training tasks and beyond...

Annotate Homepage

Feature & Support Requests

Tag Image Endpoint Enhancements

The PixLab team is pleased to announce major enhancement to the /tagimg endpoint.

The image labeling endpoint let you programmatically generates a description of an image in human readable language with complete sentences. The description is based on the visual content as reported by our state-of-the-art image labeling algorithm. More than one description can be generated for each image. Descriptions are ordered by their confidence score. All descriptions are in English.

the /tagimg endpoint documentation is available to consult here and below a working Python code sample:

import requests
import json

# Tag an image based on detected visual content which mean running a CNN on top of it.

# Target Image
img = 'https://s-media-cache-ak0.pinimg.com/originals/35/d0/f6/35d0f6ee0e40306c41cfd714c625f78e.jpg' 
# Your PixLab key
key = 'My_PixLab_Key'

req = requests.get('https://api.pixlab.io/tagimg',params={'img':img,'key':key})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    total = len(reply['tags']) # Total tags
    print ("Total tags: "+str(total))
    for tag in reply['tags']:
        print("Tag: "+tag['name']+" - Confidence: "+str(tag['confidence']))

You can visit the PixLab Github repository for additional code samples in various programming languages.