| Technical Name | Automatically Gound Truth Labeling FCU-iLabel | ||
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| Project Operator | Feng Chia University | ||
| Project Host | 陳冠宏 | ||
| Summary | We developed deep learning to mark the training set required for a large number of deep learning training which improves the accuracy of deep learning and saves lots of requirements on picture annotation. In addition, after maintaining certain stability, the graphics detection program is also simplified, which can effectively improve the speed of labeling. |
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| Scientific Breakthrough | We developed deep learning to mark the training set required for a large number of deep learning training which improves the accuracy of deep learning and saves lots of requirements on picture annotation. In addition, after maintaining certain stability, the graphics detection program is also simplified, which can effectively improve the speed of labeling. |
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| Industrial Applicability | It can provide object detection, a large number of pictures and video loading, and it can generate a large number of pictures or video training sets, which can be easily used in the training of neural networks, such as self-driving vehicle detection, humanoid detection of drones and etc. |
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| Keyword | Artificial Intelligence Ground Truth Labeler Deep Convolution Neural Network Deep Learning Object Detection Object Recognition YOLO Picture Training Set Automated Annotation Customized Weights | ||
| Notes | |||
- Contact
- dmshien@gmail.com
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