Rtx 3060 gpu

A Lenovo brand custom computer was chosen as the experimental hardware setup. The CPU used was an AMD Ryzen 5 with a clock frequency of 3.90 GHz, and the system had 16 GB of RAM. An NVIDIA RTX3060 GPU with 12 GB of dedicated memory was also installed. Python was used to build a virtual environment based on the PyTorch deep learning framework and conduct model training and performance measurement.

Pearson correlation coefficient was used to determine the relationship between two gradient results and all gradient results. The SciPy package in Python was used to calculate the Pearson correlation coefficient, where the parameters $p$ and $r$ reflect the significance level and correlation, respectively. Significant differences were considered when $p<0.05$ . The positive or negative value of $r$ reflected the positive or negative correlation. Difference analysis was used to analyze the differences of different gradient results, as well as to analyze the effect of data augmentation methods.
All the experiments were run on Intel Core i7-12700 (2.10 GHz), 64GB RAM and NVIDIA RTX3060 GPU with 12 GB memory. Python 3.7.1 was used to implement all program code. The functions in the Sckit-learn package were used to realize the KNN and SVM algorithms. The MobileViT-xs model was implemented by the deep learning framework PyTorch 1.10, and CUDA Toolkit 11.3 was used to accelerate the processing. More code details were posted at https://github.com/mepleleo/DA_peanut (accessed on 1 April 2022).

We utilized the SepConv +  + deep learning model [8 ] for IR frame interpolation due to its promising performance in video frame interpolation tasks. SepConv +  + is a lightweight model with less than 20 million parameters and capable of processing 5 to 20 frames per second. It employs a U-Net-style backbone [17 ] which initially contracts into small-scale feature maps using convolutions and then expands to the original size while preserving detailed information through skip connections. SepConv +  + extracts features to estimate two sets of convolution kernels that specify motion between the frames. These kernels were applied in the convolution process of preceding and succeeding frames, and their output features were fused to generate the interpolated intermediate frame. Training was conducted separately on the training set for each IR modality and on a combined training set comprising sequences from all three IR modalities. Implemented in PyTorch [18 ], SepConv +  + was executed on an NVIDIA RTX 3060 GPU.

The mutton multi-part classification and detection test was conducted using a customized Lenovo computer equipped with an AMD Ryzen 5 CPU operating at a dominant frequency of 3.90 GHz, 16 GB of operating memory, and an NVIDIA RTX3060 GPU with 12 GB of graphics memory. The operating system was Windows 11, and a virtual environment was established using the Python 3.8 programming language to conduct model training and performance measurements based on the PyTorch 1.8.0 deep learning framework. For GPU-accelerated computing, cuDNN 8.1.1 was used with DUDA version 11.0.