Computer vision is the most widely researched and mature technology field of deep learning, and is widely used in scenarios such as mobile phone photographing, intelligent security protection, and automated driving. Since AlexNet won the ImageNet competition in 2012, deep learning has greatly promoted the development of the computer vision field. Almost all the most advanced computer vision algorithms are related to deep learning. Deep neural network can extract image features layer by layer and retain local invariance. It is widely used in visual tasks such as classification, detection, segmentation, tracking, retrieval, recognition, promotion, and reconstruction.
This section describes how to use the customized capabilities provided by MindSpore, such as callback, metrics, and log printing, to help you quickly debug the training network.
MindSpore supports `DATA_PARALLEL` and `AUTO_PARALLEL`. Automatic parallel is a distributed parallel mode that integrates data parallel, model parallel, and hybrid parallel. It can automatically establish cost models and select a parallel mode for users.
The mixed precision training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time.
This tutorial describes the model security protection methods provided by MindArmour, helping you quickly use MindArmour and provide certain security protection capabilities for your AI model.
Sentiment classification is a subset of text classification in NLP, and is the most basic application of NLP. It is a process of analyzing and inferencing affective states and subjective information, that is, analyzing whether a person's sentiment is positive or negative.
MindSpore Predict is a lightweight deep neural network inference engine that provides the inference function for models trained by MindSpore on the device side. This tutorial describes how to use and compile MindSpore Predict.
You can convert non-standard datasets and common datasets into the MindSpore data format so that they can be easily loaded to MindSpore for training. In addition, the performance of MindSpore in some scenarios is optimized, which delivers better user experience when you use datasets in the MindSpore data format.
Data is the basis of deep learning. Data input plays an important role in the deep neural network training. Therefore, after the original dataset is obtained and before data is loaded and trained, data processing or augmentation is often required due to data size and performance restrictions, to obtain optimized data input.
MindSpore helps you load common datasets, datasets of specific data formats, or custom datasets. Before loading a dataset, you need to import the required library `mindspore.dataset`.
