inSight-mk1

inSight-mk1's Stars

• microsoft/terminal

  The new Windows Terminal and the original Windows console host, all in the same place!

  Language:C++95k1.3k12.7k8.2k

• open-mmlab/mmaction2

  OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

  Language:Python4.2k431.4k1.2k

• open-mmlab/mmdeploy

  OpenMMLab Model Deployment Framework

  Language:Python2.7k361.6k620

• VainF/Torch-Pruning

  [CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs

  Language:Python2.6k35350323

• OpenGVLab/InternImage

  [CVPR 2023 Highlight] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions

  Language:Python2.5k34263231

• baaivision/EVA

  EVA Series: Visual Representation Fantasies from BAAI

  Language:Python2.2k30157162

• IDEA-Research/DINO

  [ICLR 2023] Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"

  Language:Python2.2k31260234

• IDEA-Research/detrex

  detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.

  Language:Python2k24162206

• Sense-X/Co-DETR

  [ICCV 2023] DETRs with Collaborative Hybrid Assignments Training

  Language:Python96910162105

• DUOMO/TransGPT

  Language:Python70362676

• DataXujing/YOLOv8

  :fire: Official YOLOv8模型训练和部署

  Language:Python60961179

• cfzd/Ultra-Fast-Lane-Detection-v2

  Ultra Fast Deep Lane Detection With Hybrid Anchor Driven Ordinal Classification (TPAMI 2022)

  Language:Python5841018596

• stotiks/chia-plotter

  Language:C543131072

• Turoad/CLRNet

  Pytorch implementation of our paper "CLRNet: Cross Layer Refinement Network for Lane Detection" (CVPR2022 Acceptance).

  Language:Python4788146104

• ruohoruotsi/LSTM-Music-Genre-Classification

  Music genre classification with LSTM Recurrent Neural Nets in Keras & PyTorch

  Language:Python296151263

• Wolfwjs/GANet

  A Keypoint-based Global Association Network for Lane Detection. Accepted by CVPR 2022

  Language:C++234105129

• FishAndWasabi/YOLO-MS

  YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection

  Language:Python22162323

• iscyy/yoloair2

  ☁️💡🎈专注于改进YOLOv7，Support to improve Backbone, Neck, Head, Loss, IoU, NMS and other modules

  Language:Python1975731

• hirotomusiker/CLRerNet

  The official implementation of "CLRerNet: Improving Confidence of Lane Detection with LaneIoU"

  Language:Python16213819

• xxradon/ONNXToCaffe

  pytorch -> onnx -> caffe, pytorch to caffe, or other deep learning framework to onnx and onnx to caffe.

  Language:Python15842333

• Wulingtian/yolov5_onnx2caffe

  yolov5 onnx caffe

  Language:Python11122737

• Alibaba-MIIL/AudioClassfication

  Language:Python740813

• sarthak268/Audio_Classification_using_LSTM

  Classification of Urban Sound Audio Dataset using LSTM-based model.

  Language:Python703218

• mkang315/RCS-YOLO

  [MICCAI'23] Official implementation of "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection".

  Language:Python6841410

• Yangly0/lane-detection-v1.0

  基于Opencv的车道线检测：1. 图像加载；2.图像预处理：图片灰度化，高斯滤波；3.Cany边缘检测；4.感兴趣区域检测；5.霍夫直线检测 ；6.直线拟合；7.车道线叠加；8.图片和视频测试；9.可视化界面pyqt5 （可选）。

  Language:Python63216

• zhenghan3/Stock-Prediction-and-Quantitative-Analysis-Based-on-Machine-Learning-Method

  This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in China Securities Index 800 Index through the machine method. The investment system pipeline has been implemented including data acquirement, data preprocessing, model tuning and selection based on the XGBoost boosted tree model.

  Language:Python611119

• majiajue/Quantitative-Trading-Strategy-Based-on-Machine-Learning

  Firstly, multiple effective factors are discovered through IC value, IR value, and correlation analysis and back-testing. Then, XGBoost classification model is adopted to predict whether the stock is profitable in the next month, and the positions are adjusted monthly. The idea of mean-variance analysis is adopted for risk control, and the volatility of the statistical benchmark index (HS300 Index) is used as a threshold for risk control. Back-testing results: the annual return rate is 11.54%, and the maximum drawdown is 17.91%.

  Language:Python141011

• goksenin-uav/torchreid-pip

  Torchreid-Pip: Packaged version of Torchreid

  Language:Python10101

• inSight-mk1/YOLOU

  YOLOv3、YOLOv4、YOLOv5、YOLOv5-Lite、YOLOv6、YOLOv7、YOLOX、YOLOX-Lite、TensorRT、NCNN、Tengine、OpenVINO

  Language:Python1000

• xiya888/lane_detect_convert

  aaa

  Language:Jupyter Notebook12