I am following this tutorial on image segmentation on the TensorFlow website.

The website uses its own labeled images for the tutorial, so the images have data that says which pixels are a part of the object, which ones border the object, and which pixels aren't part of the object.

This tutorial uses the Oxford-IIIT Pet Dataset, created by Parkhi et al. The dataset consists of images of 37 pet breeds, with 200 images per breed (~100 each in the train and test split). Each image includes the corresponding labels, and pixel-wise masks. The masks are class-labels for each pixel. Each pixel is given one of three categories :

  • Class 1: Pixel belonging to the pet.
  • Class 2: Pixel bordering the pet.
  • Class 3: None of the above/ Surrounding pixel.

In my case, I have unlabelled images, so I cannot currently perform image segmentation with my images. Which approach should I use to label my images for image segmentation?


I have found the free Python library remo. It's labeling soft for Image Classification, Image Detection, and Instance Segmentation tasks.

Firstly you have to install the library:

pip install remo

then init and start:

python -m remo_app init
python -m remo_app

The Django server will be hosted and you can start labeling via opening the http://localhost:8123 link on your browser.

  • $\begingroup$ While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference. Link-only answers can become invalid if the linked page changes. - From Review $\endgroup$ Jan 18 at 4:55

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