How to accelerate data labeling processs for training YOLOv8?

Streamlining Data Labeling and Supercharging Model Training: Leveraging Anylabeling’s Cutting-Edge Features for Fast and Accurate Object Annotation, Image Segmentation, and Integration with Meta’s Segment Anything

Note: For those who don’t have a Medium account or have reached their daily article limit, you can access the article through this alternative link: https://henrynavarro.org/how-to-accelerate-data-labeling-processs-for-training-yolov8-3142d691ae5b?sk=d50689167388db5911391f05c0707674

In this blog, I will share my experience with Anylabeling, a new and innovative data labeling tool that has significantly accelerated my data labeling tasks, free of charge. With Anylabeling, I was able to train a YOLOv8 model quickly and efficiently, thanks to its integration of other open-source tools like LabelImg and Labelme.

Developed by Viet-Anh Nguyen, Anylabeling is a state-of-the-art data labeling tool that combines popular tools like Autolabeling and LabelImg with an improved user interface and auto-labeling capabilities. This unique combination of features makes Anylabeling a valuable resource for data scientists and machine learning engineers.

AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling

One of the most exciting features of Anylabeling is its integration with Segment Anything, the latest neural network released by Meta. With this integration, Anylabeling can create precise masks for objects in images, making image segmentation tasks faster and more accurate than ever before.

Steps followed

1. Data Collection
2. Data Annotation
3. Dataset structure
4. Training YOLOv8
5. Testing on real environment
6. Deployment using FlaskAPI (next tutorial)

Data Collection

Downloading images from the internet can be a time-consuming task if you have to download them one by one. Many search engine block web scraper from Python or any other language. Sometimes you can not even run a wget in your terminal because the anti bot will forbide the request.