5 results about "Model tuning" patented technology

A kind of H&E image ovarian cancer polyploid giant cell (PGCCs) detection method based on deep learning, including constructing specific data set, image annotation and data division, using OCDet model to carry out feature learning and optimization, and model training and evaluation.The method first establishes the H&E staining image data set containing PGCCs, then accurately labels the image and divides it into training, verification and test set.OCDet model takes CSPDarkNet as the core, combines ECA mechanism, focuses on the deep learning and re-encoding of pathological semantic features.Through the training data set, the model updates parameters through back propagation and gradient descent, optimizes to identify PGCCs features.The verification set is used for model tuning, and the test set is used for evaluating the performance of the model.The automatic detection technology of the application can help doctors improve the diagnosis efficiency and reduce the error, provide an important reference for clinical treatment and prognosis evaluation, and show significant clinical application value.

Embodiments of the present disclosure disclose a model training method, a dialogue reply method, devices, equipment and media. A specific embodiment of the method comprises: using a first training data set to perform model training on an initial full-duplex dialogue model to obtain a trained first full-duplex dialogue model; using a second training data set to perform model training on the first full-duplex dialogue model to obtain a trained second full-duplex dialogue model; and using a third training data set to perform model tuning on the second full-duplex dialogue model to obtain a full-duplex dialogue model, wherein the third training data is preference data for inference token granularity. This embodiment is related to artificial intelligence, and using the trained full-duplex dialogue model can achieve dynamic matching of inference granularity and problem complexity, reduce inference token redundancy for simple problems, guarantee inference depth for complex problems, reduce invalid overhead in the dialogue reply process, and improve reply efficiency.

The application relates to the technical field of data analysis, and discloses a data analysis method based on model tuning, which comprises the following steps: when a model tuning control instruction is received, selecting a plurality of groups of hyperparameter combinations from hyperparameter combinations corresponding to preset type data according to a mapping relationship between data types and hyperparameter combinations; determining a target hyperparameter combination from the plurality of groups of hyperparameter combinations based on a first algorithm; constructing an initial data analysis model based on the target hyperparameter combination; performing first model variable equivalent simplification on the initial data analysis model based on a second algorithm to generate an intermediate data analysis model; performing second model variable equivalent simplification on the intermediate data analysis model based on a third algorithm to generate a target data analysis model; and calling the target data analysis model to perform data analysis on preset type data to obtain a data analysis result. The application can effectively improve the running performance of a data analysis model and the accuracy of data analysis.

This invention discloses a method for debugging a lubricating oil system based on digital twins, comprising the following steps: 1) Constructing a simulation model of the lubricating oil system and equipment by defining a lubricating oil system equipment model library; 2) The online model receives real-time operating data of the physical lubricating oil system collected by sensors, transmits the online measured data to the simulation model to drive simulation calculation, and transmits the calculation results to the demonstration model for simulation animation display; 3) Based on the lubricating oil user flow requirements under different pre-given operating conditions, and based on virtual-real fusion simulation, the action parameters of each actuator of the lubricating oil system are calculated through offline model tuning and transmitted to the control system as virtual debugging signals; 4) The physical lubricating oil system receives the control signals from the control system to adjust and control the operation of the physical lubricating oil system. This invention, by constructing a high-precision lubricating oil system model and using digital twin-based virtual debugging technology for lubricating oil systems, can be used for efficient debugging, accurate analysis, and virtual verification of physical systems.

A device and method with model determination are provided. The method includes identifying a pre-trained first model and at least one second model tuned based on the first model, for each of layers included in the first model, identifying a first weight value of the first model and at least one second weight value of the at least one second model, for each of the layers, determining a third weight value based on a location derived through a linear interpolation using a first location corresponding to the first weight value and at least one second location corresponding to the at least one second weight value that are in a weight value space, and determining a target model based on the first model including the third weight value.