Jumabek Alikhan's starred repositories
google_vision_workspace
Integrates Google Vision features, including image labeling, face, logo, and landmark detection, optical character recognition (OCR), and detection of explicit content, into applications.
ByteTrack-yolov7-implementation
Minimal implementation of YOLOv7 based ByteTrack MOT
News-Aggregator
Django project to scrape a news website using Beautiful soup and display in our template.
Human-Action-Recognition
Computer Vision Project : Action Recognition on UCF101 Dataset
Human_Activity_Recognition
A new and computationally cheap method to perform human activity recognition using PoseNet and LSTM. Where we use PoseNet for Preprocessing and LSTM for understand the sequence.
har-joint-model
AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors
Optical-Flow-Guided-Feature
Code for paper: Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
human-action-recognition
Multi Person Skeleton Based Action Recognition and Tracking
Human-Activity-Recognition
Recognizing human activities using Deep Learning
3d-cnn-action-recognition
Implementation of Action Recognition using 3D Convnet on UCF-101 dataset.
Robyn
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
YoloV4TinyBarracuda
YOLOv4-tiny on Unity Barracuda
prince-computer-vision
work-through of Computer Vision: Models, Learning, and Inference by Simon J.D. Prince
denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
classifier-performance
Suite of algorithms and data for measuring performance of binary classifiers
home_surveillance
Home surveillance system with facial recognition
bootstrap-wysiwyg
Tiny bootstrap-compatible WYSIWYG rich text editor
gans-awesome-applications
Curated list of awesome GAN applications and demo
artemis-speaker-tools-b
Artemis Speaker Tools B
Santa20-Local-Contest
Django LB for Santa 2020, https://www.kaggle.com/c/santa-2020/overview
open-data-registry
A registry of publicly available datasets on AWS
Crosswalk-Recognition
Zebra Crosswalks Recognition for Visually Impaired People