Applied to human body posture detection, it can detect the position of people's eyes, nose, ears, neck, shoulders, elbows, wrists, hip joints, knee joints, and ank

Use DenseNet to train and inference stock data.

Jupyter-Image-GAN-Compression-PyTorch is to perform style conversion after compressing the GAN model. The compressed model not only reduces the amount of calculation, r

Use DenseNet to train and inference the Sin function.

FaceNet can be applied to face grouping and face classification, and judge the similarity of faces through Euclidean distance, and then achieve face recognition.

Use one-dimensional Convolutional neural network for data analysis.

GPT-2 is used to automatically generate an English article, and you can also enter any sentence to automatically generate an English article.

The easy-to-use tools for common image processing include image reading, image saving, grayscale conversion, binarization, and connection to find objects.

The Speech2Text APP can train audio files, use the tensorflow model to analyze the selected audio files, and output its category to reach speech to text.

The pix2pix architecture is similar to GAN, but the purpose is not to generate simulated images, but to use supervised learning to output the image style learned by the

This APP uses SRGAN to generate a High Resolution image from a Low Resolution image to improve the resolution of the image.

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 XGBoost-Regression-Time-Series to perform regression analysis of time series data.

Use LSTM (Long Short-Term Memory) to analyze time series data.

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