1_Image_Annotation

TrainYourOwnYOLO: Image Annotation

Dataset

To train the YOLO object detector on your own dataset, copy your training images to TrainYourOwnYOLO/Data/Source_Images/Training_Images. By default, this directory is pre-populated with 101 cat images. Feel free to delete all existing cat images to make your project cleaner.

Creating a Dataset from Scratch

If you do not already have an image dataset, consider using a Chrome extension such as Fatkun Batch Downloader which lets you search and download images from Google Images. For instance, you can build a fidget spinner detector by searching for images with fidget spinners.

Annotation

To make our detector learn, we first need to feed it some good training examples. We use Microsoft's Visual Object Tagging Tool (VoTT) to manually label images in our training folder TrainYourOwnYOLO/Data/Source_Images/Training_Images. To achieve decent results annotate at least 100 images. For good results label at least 300 images and for great results label 1000+ images.

Download VoTT

Head to VoTT releases and download and install the version for your operating system. Under Assets select the package for your operating system:

• vott-2.x.x-darwin.dmg for Mac users,
• vott-2.x.x-win32.exe for Windows users and
• vott-2.x.x-linux.snap for Linux users.

Installing *.snap files requires the snapd package manager which is available at snapcraft.io.

Create a New Project

Create a New Project and call it Annotations. It is highly recommended to use Annotations as your project name. If you like to use a different name for your project, you will have to modify the command line arguments of subsequent scripts accordingly.

Under Source Connection choose Add Connection and put Images as Display Name. Under Provider choose Local File System and select TrainYourOwnYOLO/Data/Source Images/Training_Images and then Save Connection. For Target Connection choose the same folder as for Source Connection. Hit Save Project to finish project creation.

Export Settings

Navigate to Export Settings in the sidebar and then change the Provider to Comma Separated Values (CSV), then hit Save Export Settings.

Labeling

First create a new tag on the right and give it a relevant tag name. In our example, we choose Cat_Face. Then draw bounding boxes around your objects. You can use the number key 1 to quickly assign the first tag to the current bounding box.

Export Results

Once you have labeled enough images press CRTL+E to export the project. You should now see a folder called vott-csv-export in the Training_Images directory. Within that folder, you should see a *.csv file called Annotations-export.csv which contains file names and bounding box coordinates.

Convert to YOLO Format

As a final step, convert the VoTT csv format to the YOLOv3 format. To do so, run the conversion script from within the TrainYourOwnYOLO/1_Image_Annotation folder:

python Convert_to_YOLO_format.py

The script generates two output files: data_train.txt located in the TrainYourOwnYOLO/Data/Source_Images/Training_Images/vott-csv-export folder and data_classes.txt located in the TrainYourOwnYOLO/Data/Model_Weights folder. To list available command line options run python Convert_to_YOLO_format.py -h.

That's all for annotation!

Next, go to TrainYourOwnYOLO/2_Training to train your YOLOv3 detector.