Errors in Training Data: How to Identify and Avoid Common Data Errors（Bias)
1. Labeling error
Labeling errors are among the most common errors in developing high-quality data, and there are several types of errors. For example, imagine if the task received is to draw a bounding box around objects in an image, the expected output is a tight bounding box around each object. The following are several types of errors that may occur in the process:
Object Missing: The labeling person did not draw a bounding box for each object.
Rough Labeling: The bounding box of each object is not tight enough, and there is an extra gap between the object and the bounding box.
In many types of projects, errors may occur, and the key to avoiding these situations is to convey clear instructions to the workers.
2. Unbalanced training data
You need to consider the composition of the training data carefully. Unbalanced data sets can lead to biases in model performance. Data imbalance will occur in the following situations:
Scene imbalance: If the data set is not representative, the category will cause imbalance. If you train your model to recognize objects but only use limited sources, your model will be trained under certain conditions. Therefore, the results under some other status will be unsatisfactory.
Timeliness of data: With the development of the world, the model will gradually degenerate. The coronavirus is a perfect example. If you search for “corona” in 2019, the headline of the search results page is likely to be Corona beer. But in 2021, the search page is full of articles about the coronavirus. Therefore, the model needs to be updated regularly based on new data so as to adapt to changes in the natural environment.
Demand for the highest quality AI training data
At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, and finance, etc. Each field has requirements for subdivision and specialization.
Among them, in particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.
In the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.
ByteBridge, a Human-powered and ML-powered Data Labeling Tooling SaaS Platform
Accuracy and Efficiency
- 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 machine and human workforce
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%
Communication Cost Saving
On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.
In addition, clients can iterate data features, attributes, and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.
As a fully managed platform, it enables developers to manage and monitor the overall data labeling process and provides API for data transfer. The platform also allows users to get involved in the QC process.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
We can provide personalized annotation tools and services according to customer requirements.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.