makesense.ai is a free to use online tool for labelling photos. Thanks to the use of a browser it does not require any complicated installation - just visit the website and you are ready to go. It also doesn't matter which operating system you're running on - we do our best to be truly cross-platform. It is perfect for small computer vision deeplearning projects, making the process of preparing a dataset much easier and faster. Prepared labels can be downloaded in one of multiple supported formats. The application was written in TypeScript and is based on React/Redux duo.

Our application is being actively developed. If you have an idea for a new functionality, please hit us on Twitter or create an issue where you can describe your concept. In the meantime, see what improvements we are planning for you in the future.

• Export labels in Pascal VOC XML format
• Optimization of the process of loading photos from disk - queuing
• Labelling objects using polygons and Bézier curves
• Labelling objects using lines
• Export labels in COCO JSON format
• Separate tab with settings
• Support basic image operations like crop and resize
• Converting video to image frames
• Keyboard shortcuts to improve productivity
• Automatic detection of objects in a photo - all you have to do is to label them
• OCR labelling
• Integration with external storage - Amazon S3, Google Drive, Dropbox
• Copy annotations from previous image into the next one

# clone repository
git clone https://github.com/SkalskiP/make-sense.git

# navigate to main dir
cd make-sense

# install dependencies
npm install

# serve with hot reload at localhost:3000
npm start

Some Windows 10 users may also have problems with running applications locally. The problems can be solved by adding additional dependencies to the project, through a command: npm install normalize.css --save. More information about this problem is available in the #16.

• A .zip package containing files in YOLO format

1 0.404528 0.543963 0.244094 0.727034
2 0.610236 0.494751 0.188976 0.437008
1 0.754921 0.791339 0.354331 0.413386

• A .zip package containing files in Pascal VOC XML format

<annotation>
    <folder>{ project_name }</folder>
    <filename>{ image_name }</filename>
    <path>{ /project_name/file_name }</path>
    <source>
        <database>Unspecified</database>
    </source>
    <size>
        <width>{ image_width }</width>
        <height>{ image_height }</height>
        <depth>3</depth>
    </size>
    <object>
        <name>{ label_name }</name>
        <pose>Unspecified</pose>
        <truncated>Unspecified</truncated>
        <difficult>Unspecified</difficult>
        <bndbox>
            <xmin>{ rect_left }</xmin>
            <ymin>{ rect_top }</ymin>
            <xmax>{ rect_right }</xmax>
            <ymax>{ rect_bottom }</ymax>
        </bndbox>
    </object>
</annotation>

<annotation>
	<folder>my-project-name</folder>
	<filename>000007.jpg</filename>
	<path>/my-project-name/000007.jpg</path>
	<source>
		<database>Unspecified</database>
	</source>
	<size>
		<width>1280</width>
		<height>960</height>
		<depth>3</depth>
	</size>
	<object>
		<name>kiwi</name>
		<pose>Unspecified</pose>
		<truncated>Unspecified</truncated>
		<difficult>Unspecified</difficult>
		<bndbox>
			<xmin>208</xmin>
			<ymin>486</ymin>
			<xmax>497</xmax>
			<ymax>718</ymax>
		</bndbox>
	</object>
	<object>
		<name>banaba</name>
		<pose>Unspecified</pose>
		<truncated>Unspecified</truncated>
		<difficult>Unspecified</difficult>
		<bndbox>
			<xmin>643</xmin>
			<ymin>118</ymin>
			<xmax>1178</xmax>
			<ymax>799</ymax>
		</bndbox>
	</object>
</annotation>

• Single CSV file

banana,491,164,530,614,000000.jpg,1280,960
banana,462,245,466,353,000001.jpg,1280,960
banana,542,477,587,375,000001.jpg,1280,960
banana,636,109,561,695,000007.jpg,1280,960
kiwi,198,477,317,251,000007.jpg,1280,960
kiwi,558,423,219,222,000008.jpg,1280,960
kiwi,758,360,252,236,000008.jpg,1280,960

We don't store your images, because we don't send them anywhere in the first place.

This project is licensed under the GPL-3.0 License - see the LICENSE file for details

Copyright (c) 2019-present, Piotr Skalski