https://docs.khadas.com/ 2026-04-06T16:55:14+00:00 https://docs.khadas.com/ https://docs.khadas.com/ttps://docs.khadas.com/lib/tpl/dokuwiki-new/images/favicon.ico text/html 2026-04-02T02:46:43+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/densenet?rev=1775112403&do=diff npu densenet vim4 onnx Doc for version ddk-3.4.7.7 DenseNet CTC ONNX Keras VIM4 Demo - 3 Introduction Densenet_CTC is a text recognition model. It only can recognize single line text. Therefore usually, it needs to be used in conjunction with a text detection model. text/html 2025-06-12T05:38:13+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/face-recognition?rev=1749721093&do=diff npu retinaface facenet vim4 Doc for version ddk-3.4.7.7 Face Recognition VIM4 Demo - 7 Face Recognition Demo consists of two models, RetinaFace and FaceNet. It can detect face on image and recognize who it is. Here are two judgment indicators, cosine similarity and Euclidean distance. The closer the cosine similarity is to 1 and the closer the Euclidean distance is to 0, the more similar is between two faces. text/html 2026-04-02T02:47:20+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/facenet?rev=1775112440&do=diff npu facenet vim4 pytorch Doc for version ddk-3.4.7.7 FaceNet PyTorch VIM4 Demo - 6 Introduction FaceNet is a face recognition model. It will convert a face image into a feature map. Compare the feature map between image and face database. Here are two judgment indicators, cosine similarity and Euclidean distance. The closer the cosine similarity is to 1 and the closer the Euclidean distance is to 0, the more similar is between two faces. text/html 2026-04-02T02:47:10+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/retinaface?rev=1775112430&do=diff npu retinaface vim4 pytorch Doc for version ddk-3.4.7.7 RetinaFace PyTorch VIM4 Demo - 5 Introduction RetinaFace is a face detection model. It can draw five key points on each face, including two eyes, nose and two corners of mouth. Inference results on VIM4. Inference speed test text/html 2026-04-02T02:46:53+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/vgg16?rev=1775112413&do=diff npu vgg16 vim4 tensorflow keras Doc for version ddk-3.4.7.7 VGG16 TensorFlow Keras VIM4 Demo 4 Introduction VGG16 is a classification model. It can assign a single label to an entire image. Image and inference results on VIM4. VGG16 is a convolution neural net architecture that’s used for image recognition. It utilizes 16 layers with weights and is considered one of the best vision model architectures to date. text/html 2026-04-02T02:46:23+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/yolov7-tiny?rev=1775112383&do=diff npu yolo opencv vim4 Doc for version ddk-3.4.7.7 YOLOv7-tiny VIM4 Demo - 1 Introduction YOLOv7-Tiny is an object detection model. It uses bounding boxes to precisely draw each object in image. Inference results on VIM4. Inference speed test: USB camera about 126ms text/html 2026-04-02T02:47:43+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/yolov8n-pose?rev=1775112463&do=diff npu yolo opencv vim4 Doc for version ddk-3.4.7.7 YOLOv8n-Pose OpenCV VIM4 Demo - 8 Introduction YOLOv8n-Pose inherits the powerful object detection backbone and neck architecture of YOLOv8n. It extends the standard YOLOv8n object detection model by integrating dedicated pose estimation layers onto its head. This allows it to not only detect people (bboxes) but also simultaneously predict the spatial positions (keypoints) of their anatomical joints (e.g., shoulders, elbows, knees, ankles). text/html 2026-04-02T02:46:34+00:00 Anonymous (anonymous@undisclosed.example.com) https://docs.khadas.com/products/sbc/vim4/npu/demos/yolov8n?rev=1775112394&do=diff npu yolo opencv vim4 Doc for version ddk-3.4.7.7 YOLOv8n OpenCV VIM4 Demo - 2 Introduction YOLOv8n is an object detection model. It uses bounding boxes to precisely draw each object in image. Inference results on VIM4. Inference speed test: USB camera about 150ms