CycleGAN is a well-known algorithm published in ICCV2017 image-to-image translation. The biggest feature is that it does not require training data to be paired.
Human body posture detection, detecting the position of human nose, eyes, ears, neck, shoulders, elbows, wrists, hip joints, knee joints, and ankles.
FaceNet directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. The model does not
Model conversion, from yolo PyTorh model to ONNX model, or Tensorflow 1.X model to ONNX model.
Use UNet for image segmentation, as shown in the figure above, which can be applied to medical image segmentation. This example is Chest CT image segmentation.
Use CSPResNeXt50 for image classification. An example is to use steel plate defect classification. You can use this APP to train your images for image classification.
Use XGBoost Classification for data regression analysis.
This APP is an application with multiple image annotation tools. It mainly includes the common LabelImg, Labelme, the Annotation tool developed by LEADERG, and supports
Use XGBoost Regression for data regression analysis.
Image segmentation with MaskRCNN.
The YOLOv4 APP can be applied to factory defect detection, medical image analysis, biological image analysis, industrial safety image analysis, mask image analysis, etc