Engineers want an AI model to identify objects accurately, and the best way is not to use the code to describe an object, but build a good deep machine learning model to recognize an object. Engineers can use images in which objects have been labeled, then feed them into the AI model training.
Data labeling technique is used to make the objects recognizable and understandable for machine learning models. It is critical for the development of machine learning (ML) industries such as face recognition, autonomous driving, aerial drones, and many other AI and robotics applications.
At present, data collection and labeling are mainly divided into visual (picture and video), audio, and text data.
The principle of Machine learning technology can be understood with the example of a big man teaching a small child:
- Visual — used to train the image recognition system, which is equivalent to adults using pictures to show kids text or using cartoons (videos) to teach children to understand various objects.
- Audio — used to train an audio recognition system, which is equivalent to teaching children to talk through conversations.
- Text — Used to train systems such as semantic comprehension, equivalent to teaching a child to read.
How fast a child learns depends on two things:
1. “The child’s talent”
2. “The number of times for cognitive enhancement”
1. The quality of the algorithm model
2. The quantity and quality of training data
Now many companies in the field of AI are using similar algorithms, many even are using the same open-source project.
In other words, the amount of data and the quality of the data used in algorism training can play a decisive role.
In addition to visual recognition, there is also a huge need for data collection and labeling in areas such as speech recognition and text recognition.
Behind this is a large number of scene-based audio training, such as the special distinction of kids’ and elders’ voices, local dialects, accents, outdoor noise marking, etc…
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- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
- All work results are completely screened and inspected by the human workforce.
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.
In addition, we can provide personalized annotation tools and services according to customer requirements.
- Using an AI-assisted tool can improve efficiency multiple times as the pre-labeling gains time.
Flexibility: More Engaged in the Labeling Loop
- On ByteBridg’s dashboard, developers can set labeling rules directly, check the ongoing process simultaneously on a pay-per-task model with a clear estimated time and price.
- Once task flow well settled, the project can start after review in 1 business day. A medium-level project with 10,000 image labeling will take less than 1 working day.
For example, you can choose a Bounding Box and Classification Template on the dashboard:
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
Ease of use
- The easy-to-integrate API enables the continuous feeding of high-quality data into a new application system.
- A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
We can provide personalized annotation tools and services according to customer requirements.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: email@example.com
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