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Hi 👋, I'm Hu Kai

愿我们永远年轻,永远热泪盈眶!

  • 会不定期分享一些自己学习的东西。🤔
  • 对计算机视觉非常感兴趣,目前主要学习2D目标检测和图像分割技术。🤪
  • 学习MMDetection、MMSegmentation和异常检测中...🎊
  • 我也很喜欢写博客,欢迎大家光临我的博客 CSDN Blog

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Hu Kai's Projects

multiyolov5 icon multiyolov5

joint detection and semantic segmentation, based on ultralytics/yolov5,

mvision icon mvision

机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶

nlp-tutorial icon nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

orb_slam2_ssd_semantic icon orb_slam2_ssd_semantic

动态语义SLAM 目标检测+VSLAM+光流/多视角几何动态物体检测+octomap地图+目标数据库

paddleocr icon paddleocr

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

perception-for-self-driving icon perception-for-self-driving

Perception algorithms for Self-driving car; Lane Line Finding, Vehicle Detection, Traffic Sign Classification algorithm.

pylearn icon pylearn

从零开始重学python,个人笔记整理。

semantic-segmentation icon semantic-segmentation

采用Pytorch的入门语义分割项目,支持的网络有Unet和Segnet;遥感语义分割;Unet;Segnet;Remote sensing semantic segmentation;

simpleaicv-pytorch-imagenet-coco-training icon simpleaicv-pytorch-imagenet-coco-training

SimpleAICV:pytorch training example on ImageNet(ILSVRC2012)/COCO2017/VOC2007+2012 datasets.Include ResNet/DarkNet/RetinaNet/FCOS/CenterNet/TTFNet/YOLOv3/YOLOv4/YOLOv5/YOLOX.

tinynn icon tinynn

A lightweight deep learning library

yoloair icon yoloair

🔥🔥🔥YOLOv5, YOLOv6, YOLOv7, PPYOLOE, YOLOX, YOLOR, YOLOv4, YOLOv3, PPYOLO, PPYOLOv2, Transformer, Attention, TOOD and Improved-YOLOv5-YOLOv7... Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

yolodet-pytorch icon yolodet-pytorch

reproduce the YOLO series of papers in pytorch, including YOLOv4, PP-YOLO, YOLOv5,YOLOv3, etc.

yolov5 icon yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

yolov5-lite icon yolov5-lite

🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

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