Avneet's starred repositories
mlforecast
Scalable machine 🤖 learning for time series forecasting.
neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Spark-Streaming-In-Python
Apache Spark 3 - Structured Streaming Course Material
Deep-learning-books-1
Books for machine learning, deep learning, math, NLP, CV, RL, etc
License_Plate_Detection_Pytorch
A two stage lightweight and high performance license plate recognition in MTCNN and LPRNet
License-Plate-Detector
基于Yolov5车牌检测,更快更准.
Real_Time_Helmet_Detection
Helmet Detector based on the CenterNet.
Spatial-Temporal-Re-identification
[AAAI 2019] Spatial Temporal Re-identification
Deep-SORT-YOLOv4
People detection and optional tracking with Tensorflow backend.
Multi-Camera-Person-Tracking-and-Re-Identification
Simple model to Track and Re-identify individuals in different cameras/videos.(Yolov3 & Yolov4)
Multi-Camera-Live-Object-Tracking
Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.
multiple-camera_multiple-people_tracking
Multiple-camera Multiple-people Tracking System for L'Oreal retail store business
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
Code for 3rd Place Solution in Face Anti-spoofing Attack Detection Challenge @ CVPR2019,model only 0.35M!!! 1.88ms(CPU)
face.evoLVe
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
dlstreamer
This repository is a home to Intel® Deep Learning Streamer (Intel® DL Streamer) Pipeline Framework. Pipeline Framework is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines.
yolov4-deepsort
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
falldetection_openpifpaf
Fall Detection using OpenPifPaf's Human Pose Estimation model
Fully-Automated-red-light-Violation-Detection
Detecting and Tracking the violating cars using yolov3 model and various image processing techniques.
Traffic-Rules-Violation-Detection
The System consists of two main components. Vehicle detection model and A graphical user interface (GUI)