Specifically, data labeling is to classify, frame, label and mark data with specific tools, which makes data more standardized and structured, thus facilitating machine learning algorithm training and model construction. The basic forms of data annotation include classification annotation, target detection, semantic segmentation and key point annotation.
Through data annotation, high-quality training data sets can be generated, and then the performance and application effect of the model can be improved.
Jinglianwen Technology is the head data provider of AI basic industry, which can help artificial intelligence enterprises solve the corresponding problems of data labeling in the whole artificial intelligence chain.
The self-developed data annotation platform covers most mainstream annotation tools. After years of polishing, the interaction is smooth and efficient. Support computer vision (framing annotation, semantic segmentation, 3D point cloud annotation, key point annotation, line annotation, 2D/3D fusion annotation, target tracking, picture classification, etc.). ), voice engineering (voice cutting, ASR voice transcription, voice emotion judgment, voiceprint recognition and labeling, etc. ), natural language processing (OCR transcription, text information extraction, NLU sentence generalization, part-of-speech tagging, machine translation, etc. ).
According to the difficulty of the project, the project manager and label team with many years of NLP label project management experience are equipped; According to the project requirements, the project structure is analyzed. Based on the principle of WBS, the project is decomposed into tree diagram layer by layer according to its internal structure and the order of implementation process, thus forming a relatively independent project unit that is easy to manage and check. The responsibility and progress of the project are implemented to each participant of the project to ensure the labeling quality.
Jinglianwen Science and Technology Data Labeling Platform opens the closed loop of data, carries out data distribution, cleaning, labeling, quality inspection and delivery in an orderly manner, strictly monitors the progress of the project, ensures the qualified data quality, greatly accelerates the landing iteration cycle of artificial intelligence-related applications, improves the training efficiency of AI data related to autonomous driving of enterprises, and promotes the rapid development of the autonomous driving industry.
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