Various codes and scripts used during AI research. Orginally developed in the Binary_label_predictions_with_CNNs repository
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Updated
May 21, 2024 - Python
Various codes and scripts used during AI research. Orginally developed in the Binary_label_predictions_with_CNNs repository
Various codes and scripts used during AI research, all neatly organised
💎A high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations, can easily install via pip.
More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam,dcn and so on), and tensorrt
This code includes classification and detection tasks in Computer Vision, and semantic segmentation task will be added later.
YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)
Official implementation of "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection".
Light-weight Single Person Pose Estimator
Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
Implemented multiple face detection algorithms to accurately count and save recognized faces in a designated folder, enhancing detection accuracy. Integrated ShuffleNet and MTCNN successfully. Developed intelligent graphics for project analysis in Excel. Implemented facial recognition using PCA and Eigenfaces for dataset matching.
Implemented multiple face detection algorithms to accurately count and save recognized faces in a designated folder, enhancing detection accuracy. Integrated ShuffleNet and MTCNN successfully. Developed intelligent graphics for project analysis in Excel. Implemented facial recognition using PCA and Eigenfaces for dataset matching.
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
A notebook to learn the use of CNNs and ShuffleNet
Architectures of convolutional neural networks for image classification in PyTorch
Models for Computer Vision
Image Classification Training Framework for Network Distillation
PyTorch implementation for 3D CNN models for medical image data (1 channel gray scale images).
Implemented the training and inference of several common deep learning model algorithms with tensorflow and pytorch.
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