4results about How to "Fully trained" patented technology

The application discloses a training method and system for improving the honesty of a large model with a small amount of labeled data, and a lightweight network is newly added to assist the LLM in self-labeling data, thereby significantly expanding the data scale available for model training on the basis of limited labeled data, so that the model can still be fully trained and optimized under the condition of limited data resources. A close cooperative working mechanism is formed between the lightweight network and the LLM, the lightweight network generates reliable labeling information for unlabeled data by performing honesty evaluation on the answers generated by the LLM, this process not only fully utilizes the probability evaluation ability of the LLM itself, but also combines the quantitative evaluation of the lightweight network on the honesty, so that the model can learn a more honest answer strategy under the condition of limited data, and the purpose of enhancing the honesty of the LLM by using a small amount of data is achieved.

The invention discloses a remote sensing image water body extraction method and system based on an RGB + X data view angle, and relates to the technical field of computer vision and remote sensing. The remote sensing image water body extraction method and system based on the RGB + X data view angle mainly comprises the following steps: extracting an X modal feature image according to red, green, blue and near-infrared bands of a remote sensing image; a dual-complexity backbone network and a hybrid fusion module are utilized to extract multi-modal, multi-scale and global features, a multi-scale MLP decoder is utilized to obtain a water body segmentation result, and up-sampling is performed to obtain a water body extraction result of original image resolution. According to the remote sensing image water body extraction method and system based on the RGB + X data view angle, the precision and generalization ability of water body extraction can be improved.

The invention discloses a multi-sight-distance deep learning track line detection method based on global features, and the method comprises the steps: 1, installing a far-focus camera and a near-focus camera at the head or side surface of a train, obtaining track line images in different scenes in the train operation process, and constructing a data set; 2, in image preprocessing, a Sobel operator is used for calculating gradient energy to generate a focusing degree image, a decision diagram is generated through multi-scale morphological optimization operation, and smooth transition of fused image boundaries is achieved through Gaussian weighted fusion; step 3, constructing an improved ResNet18 backbone network, fusing multi-scale features, combining row selection and structure perception loss, and performing Adam optimization training; and 4, real-time track line detection is carried out. According to the invention, through multi-sight-distance image fusion and global feature extraction, technical support is provided for realizing high-precision and real-time detection of a train track line, automatic train driving, track state monitoring and other key tasks.

The embodiment of the present disclosure relates to a network search method and device, electronic equipment and storage medium, and relates to the technical field of network search. The network search method comprises the following steps: obtaining an intermediate network by training in a super network, and training the intermediate network based on a kernel size to obtain a trained intermediate network; performing depth search training on the trained intermediate network by determining a learning rate and an adaptive search module for adjusting a depth selection space to obtain a depth network; and performing width search training on the depth network by determining a learning rate and an adaptive search module for adjusting a width selection space to perform network search. The technical solution in the embodiment of the present disclosure can improve the accuracy of the network.