Top 7 Benefits of Using an Image Labeling Tool in AI and Machine Learning
Recently, the world has been in a stir with the appearance of a highly intelligent AI chatbot — Chat GPT. More interestingly, Microsoft has announced that they will launch a new version of chat engine and internet browser using AI: Brad to compete with its rival, Chat GPT. The emergence of these two chatbots has heated up the AI industry more than ever before, draw a bright future growth of this sector. In order for AI to learn “the ground truth”, “gain knowledge”, then provide answers and prediction, it needs to be fed with quality labeled data which have to be labeled differently.
Today, VinLab introduce the top 7 benefits of using an Image Labeling Tool in AI and Machine Learning, some quality open-source and top 5 free labelling tools.
What is an image labeling tool?
An image labeling tool is a software application that allows users to manually annotate or tag images with descriptive labels or tags.
Image labeling is an important pre-processing step in many artificial intelligence and machine learning applications, particularly in computer vision and image classification tasks.
Labeled image data is used to train machine learning models to identify objects, scenes, and other features within images, and is critical for developing accurate and effective AI systems. Image labeling enables machines to learn from human-annotated data, making it possible to automate image recognition and analysis tasks and improve their accuracy over time.
The benefits of Image labeling platforms
Some benefits of image labeling platform in your team and company that have to mention
1. Increased accuracy and consistency
Image data labeling tools can help increase the accuracy and consistency of image data labeling by automating some of the tasks and providing quality control features.
2. Faster labeling process
An image data labeling tool can help speed up the labeling process by providing an easy-to-use interface for annotators and automating repetitive tasks. The amount of image data grows over time while people have limited time and resources.
3. Improved collaboration
Image data labeling tools often provide collaboration features that allow multiple annotators to work on the same dataset, increasing efficiency and reducing the time required to complete the project.
4. Better data management
Image data labeling tools provide a centralized location to store and manage image data and annotations, making it easier to track progress, resolve issues, and ensure quality control.
5. Enhanced security
Image data labeling tools typically provide security features that help ensure the confidentiality of sensitive data and prevent unauthorized access.
6. Cost savings
Image data labeling tools can help reduce labor costs by automating repetitive tasks and improving the efficiency of the labeling process.
7. Better scalability
Image data labeling tools can be easily scaled to accommodate large datasets, making it easier to handle large-scale projects.
Some free medical data labeling tools
Based on some key factors such as free, user friendly, accuracy, speed, scalability. We introduce some free data labeling tools special in medical fields that can meet your needs and your project requirements.
Here are top 5 medical data labeling tools that can accelerate your AI project below.
- 3D slicer
- VinLab
- ITK-Snap
- Ango.ai
- Md.ai
For more information: https://vinlab.medium.com/top-5-free-medical-image-labeling-tool-for-machine-learning-in-2023-9024338f8011
Some free opensource data labeling tool
We all know that open-source code is an openly accessible source code that anyone may review, edit and improve. It’s not only high quality because the code will be seen and critiqued by a community of people but also saving cost for your enterprises because we are not obligated to pay for the use. Besides, some benefits such as: securities, freedom and flexibility are some advantages that should be taken into consideration.
Here are top 4 free open source medical data labeling platforms with high-quality datasets and algorithms with lean process and advanced annotation features below.
- VinLab
- CVAT
- Labelme
- Labellmg
For more information: https://vinlab.medium.com/top-4-free-open-sources-medical-data-labeling-platforms-ea3dd2d6eea5
Thanks for reading!
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Contact
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Open source project: https://github.com/vinbigdata-medical/vindr-lab
