A powerful toolkit for generating LabelMe format JSON annotations using trained models (.pt or .onnx). Perfect for creating training datasets from your existing computer vision models. auto-annotation ...
a<-as.matrix(GetAssayData(object = sce_umap@assays$RNA,layer="counts")[1:20,1:20]) b<-as.matrix(GetAssayData(object = sce_umap@assays$RNA,layer="data")[1:20,1:20]) ...
Abstract: Nowadays, with the rapid growth of imaging and social network, huge volumes of image data are produced and shared on social media. Social image annotation has been an important and ...
Artificial intelligence (AI) is transforming industries across the globe, automating operations, personalizing customer experiences, and enabling unprecedented insights. From autonomous vehicles to ...
A recent survey of 6,000 consumers revealed something intriguing: while only around 33% of people think they use AI, a remarkable 77% are, in fact, using AI-powered services or devices in their daily ...
Data annotation is like giving labels to raw data so machines can understand it. Just like we use sticky notes to organise our thoughts, machines need labels to make sense of the world. These labels ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results