xxlovess

xxlovess's Stars

• smartbugs/smartbugs

  SmartBugs: A Framework to Analyze Ethereum Smart Contracts

  Language:Python574139

• Messi-Q/GPSCVulDetector

  Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection (TKDE Accepted)

  Language:Python10217

• qiongxiao/VulDeeSmartContract

  Smart Contract Vulnerability Detection Based on Deep Learning. (LSTM, BLSTM, BLSTM+Attention)

  Language:Python263

• Messi-Q/GNNSCVulDetector

  Smart Contract Vulnerability Detection Using Graph Neural Networks (IJCAI-20 Accepted)

  Language:Solidity13026

• MapleHan/cubemx_adc

  基于STM32CubeMX的ADC配置例程

  Language:C++1

• HZ1213825/HAL_STM32F4_IIC

  模拟IIC主从机

  Language:C73

• shuo-young/BlockWatchdog

  Attacker Contract Identifier Through Static Dataflow Analysis

  Language:Python20

• spyyes/PhraseAnalysis

  数据仓库与数据挖掘 大作业 -- 频繁模式挖掘

  Language:Python28

• noahcao/OC_SORT

  [CVPR2023] The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

  Language:Python786106

• ddz16/py-motmetrics

  Language:Python415

• iscyy/yoloair

  🔥🔥🔥 专注于YOLOv5，YOLOv7、YOLOv8、YOLOv9改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

  Language:Python2.5k430

• pingyang1117/SMILEtrack_Official

  SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

  Language:Python584

• NirAharon/BoT-SORT

  BoT-SORT: Robust Associations Multi-Pedestrian Tracking

  Language:Jupyter Notebook933422

• corfyi/UCMCTrack

  [AAAI 2024] UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation. UCMCTrack achieves SOTA on MOT17 using estimated camera parameters.

  Language:Python27926

• FarV-Ma/ASLAR-Res

  “ASL Alphabet Reconition - ResNet" 基于Resnet50的美式手语字母识别模块（图像分类法）。A module for American Sign Language Alphabet taeget recognition, based on image classfication model with the Resnet-50 Residental CNN.

  Language:Jupyter Notebook1

• dendorferpatrick/MOTChallengeEvalKit

  Language:Python26342

• Zxl19990529/Underwater-Multiple-Object-Tracking-Dataset

  Underwater MOT: Underwater Multiple Object Tracking dataset

  Language:Python11

• HumanSignal/awesome-data-labeling

  A curated list of awesome data labeling tools

  3.8k436

• cvat-ai/cvat

  Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

  Language:Python12.6k3k

• VisDrone/VisDrone-Dataset

  The dataset for drone based detection and tracking is released, including both image/video, and annotations.

  1.3k162

• JonathonLuiten/TrackEval

  HOTA (and other) evaluation metrics for Multi-Object Tracking (MOT).

  Language:Python968241

• MCG-NJU/MeMOTR

  [ICCV 2023] MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking

  Language:Python15711

• kskin/WaterGAN

  Source code for "WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images"

  Language:Python16148

• ultralytics/ultralytics

  Ultralytics YOLO11 🚀

  Language:Python32.5k6.3k

• mikel-brostrom/boxmot

  BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

  Language:Python6.7k1.7k

• wqk666999/CNN-LSTM-Attention

  使用卷积神经网络-长短期记忆网络（bi-LSTM）-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.

  23042

• RobinLuoNanjing/air_pollutants_prediction_lstm

  This is a project for predicting air pollutants in London by time series model, including lstm, bilstm, Convlstm, attention lstm, lightGBM and ARIMA

  Language:Jupyter Notebook13531

• yangwohenmai/LSTM

  基于LSTM的时间序列预测研究

  Language:Python2.8k700

• Alro10/deep-learning-time-series

  List of papers, code and experiments using deep learning for time series forecasting

  Language:Jupyter Notebook2.6k522

• aprbw/traffic_prediction

  Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

  Language:TeX27946