Update README.md

Author: robmarkcole

README.md

@@ -50,7 +50,6 @@ For supervised machine learning, you will require annotated images. For example
 * [Label-Detect](https://github.com/Jakaria08/Label-Detect) -> is a graphical image annotation tool and using this tool a user can also train and test large satellite images, fork of the popular labelImg tool
 * [Swipe-Labeler](https://github.com/spaceml-org/Swipe-Labeler) -> Swipe Labeler is a Graphical User Interface based tool that allows rapid labeling of image data
 * [dash_doodler](https://github.com/dbuscombe-usgs/dash_doodler) -> A web application built with plotly/dash for image segmentation with minimal supervision
-* [remo](https://remo.ai) -> A webapp and Python library that lets you explore and control your image datasets
 * TensorFlow Object Detection API provides a [handy utility](https://github.com/tensorflow/models/blob/6a55ecdea7afda51f9dc42dc17104bd6444395d9/research/object_detection/utils/colab_utils.py#L384) for object annotation within Google Colab notebooks. See usage [here](https://github.com/yasserius/tf2-object-detection-api#label-images-in-colab)
 * [coco-annotator](https://github.com/jsbroks/coco-annotator) -> Web-based image segmentation tool for object detection, localization, and keypoints
 * [pylabel](https://github.com/pylabel-project/pylabel) -> Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo. PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model