You may give the following pages a visit:

Hope you find it useful.

All the best.

http://stackoverflow.com/questions/8317787/image-labelling-and-annotation-tool

http://alternativesite.net/labelme.csail.mit.edu

Thanks a lot Sir, for your reply.

You may kindly upvote if you find it useful ^_^.

This is a good question.

In addition to the excellent observations already given, you may want to consider another approach to annotating images provided by PDF Annotator:

https://www.pdfannotator.com/en/?gclid=Cj0KEQjw-YO7BRDwi6Stp7T296ABEiQAD6iWMVWLqx-F8QMq6p9eo2EyxqisZGdT0z3hd54FPufE3zkaAoLW8P8HAQ

With this tool, you can do the following:

1.  open a worksheet that is saved as a pdf file.

2.  insert any image into the worksheet.

3.  use the tools shown in the menu bar and in the sidebar shown in the attached image.

4.  On a PC, use the snipping tool to extract an annoted image that you have created on a PDF Annotator worksheet.   Save you annotated image in a convenient place.

The snipping tool can also be used to solve the problem of downloading annotated images from Labelme, i.e., instead of downloading from Labelme, use the snipping tool to make a copy of a Labelme image.

Thanks a lot James. I really appreciate your response.

Two other means, of manually labeling an image are:

1. Microsoft Paint

2. Adobe Photoshop

I recently started a project I'm calling LabelD (https://sweppner.github.io/labeld/) that uses Annotorious + NodeJS. Check it out! Still very alpha, but the annotation is up and running! It also has a built in keyword search function sourcing images from Imgur to help build custom annotation training sets. 

Try Labelbox (www.labelbox.io).

Labelbox is a cloud based tool to easily label data.

Labelbox makes it really easy to do basic image classification or segmentation tasks. Simply upload a CSV file pointing to the location of your data and choose an image classification or segmentation labeling template to get started.

In addition, It supports any kind of data such as text, images, maps, videos and even point clouds or medical DICOM. One can create a custom labeling interface with HTML and javascript and can be as simple as 30 lines of code. You can see their template code here: https://github.com/Labelbox/Labelbox

Additional feature includes project management, team management and performance metrics.

Try CVAT (https://github.com/opencv/cvat).

Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms. It was inspired by Vatic free, online, interactive video annotation tool. CVAT has many powerful features: interpolation of bounding boxes between key frames, automatic annotation using TensorFlow OD API, shortcuts for most of critical actions, dashboard with a list of annotation tasks, LDAP and basic authorization, etc… It was created for and used by a professional data annotation team. UX and UI were optimized especially for computer vision tasks.

You can try DatTurks (https://dataturks.com/) . Free for academic research and educational use cases.

Here is a demo project to try out the image segmentation :

https://dataturks.com/projects/Dataturks/Demo%20Image%20Polygon%20Bounding%20Box

Here is a how to about setting up project and starting :

https://dataturks.com/help/image-polygon-bounding-box-annotation.php

Also, there are multiple open projects with already labeled data :

https://dataturks.com/projects/trending?type=IMAGE_POLYGON_BOUNDING_BOX_V2

Another easy to use software is Colabeler (http://www.colabeler.com/) which is available for different operating systems as well.

Diffgram https://diffgram.com (where I work).

• Workflow. Manage tasks at scale with Jobs.
• Validation. Pre-label images with your own model or use built in automatic training.
• Video support. Video frames with interpolation can save a ton of time!

Diffgram is web based:

• Fast to get started - create an account and start in moments.
• Share work with teammates for review

We have an integrated SDK that makes it easier to import your data. https://medium.com/diffgram/how-to-validate-your-deep-learning-model-with-the-diffgram-sdk-tutorial-22234a9a35 https://github.com/diffgram/diffgram/tree/master/sdk https://medium.com/diffgram/fast-annotation-net-a-framework-for-active-learning-in-2018-1c75d6b4af92

If someone has a suggestion for a very minimal tool, that quickly allows me to inspect and correct the model output, I would be happy

In other words was thinking like Viewnior (http://siyanpanayotov.com/project/viewnior) viewer behavior of going through images in a folder, and just a pencil option to either include or exclude the painted region to the final mask (2 colors), see e.g. the attachment.

Many of the suggested options are needlessly heavy for quick validation of the model when you do not have really an advanced data pipeline (yet)