Most of the models were converted by Lite.AI, and others were referenced from third-party libraries. The name of the class here will be different from the original repository, because different repositories have different implementations of the same algorithm. For example, ArcFace in [insightface](https://github.com/deepinsight/insightface) is different from ArcFace in [face.evoLVe.PyTorch](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch) . ArcFace in [insightface](https://github.com/deepinsight/insightface) uses Arc-Loss + Softmax, while ArcFace in [face.evoLVe.PyTorch](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch) uses Arc-Loss + Focal-Loss. Lite.AI uses naming to make the necessary distinctions between models from different sources. Therefore, in Lite.AI, different names of the same algorithm mean that the corresponding models come from different repositories, different implementations, or use different training data, etc. Just jump to [lite.ai-demos](https://github.com/DefTruth/lite.ai/tree/main/examples/lite/cv) to figure out the usage of each model in Lite.AI. ✅ means passed the test and ⚠️ means not implements yet but coming soon. For models which denoted ✅, you can use it through *lite::cv::Type::Model* syntax, such as *[lite::cv::detection::YoloV5](#refer-anchor-object-detection)* or *[lite::cv::face::detect::UltraFace](#refer-anchor-face-detection)*. More details can be found at [Examples for Lite.AI](#refer-anchor-Examples-for-Lite.AI) .
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