uelordi01

Unai's starred repositories

3D-Tracking-MVS

3D position tracking for soccer players with multi-camera videos

Language:Python9800

GroundingDINO

Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

Language:PythonApache-2.0526600

mlops-zoomcamp

Free MLOps course from DataTalks.Club

Language:Jupyter Notebook1037700

Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

Language:Jupyter Notebook430400

football-stats

Tracking of football players and analysis

Language:PythonGPL-3.09300

narya

The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository contains the implementation of the flowing paper https://arxiv.org/abs/2101.05388. We also make available all of our pretrained agents, and the datasets we used as well.

Language:Jupyter NotebookMIT15900

awesome-sports-camera-calibration

A collection of resources on Sports Camera Calibration

CC0-1.011900

muzic

Muzic: Music Understanding and Generation with Artificial Intelligence

Language:PythonMIT428000

bark

🔊 Text-Prompted Generative Audio Model

Language:Jupyter NotebookMIT3318200

ViTPose

The official repo for [NeurIPS'22] "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation" and [TPAMI'23] "ViTPose++: Vision Transformer for Generic Body Pose Estimation"

Language:PythonApache-2.0121500

IDM-VTON

IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild

Language:Python266800

zest_code

This is the official implementation of ZeST

Language:Jupyter NotebookMIT30300

TransPose

PyTorch Implementation for "TransPose: Keypoint localization via Transformer", ICCV 2021.

Language:Jupyter NotebookMIT34800

PCT

This is an official implementation of our CVPR 2023 paper "Human Pose as Compositional Tokens" (https://arxiv.org/pdf/2303.11638.pdf)

Language:PythonMIT26900

DearPyGui

Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies

Language:C++MIT1244300

kalman-cpp

Basic Kalman filter implementation in C++ using Eigen

Language:C++MIT49700

imgui

Dear ImGui: Bloat-free Graphical User interface for C++ with minimal dependencies

Language:C++MIT5665700

onnxruntime

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Language:C++MIT1307900

onnxruntime-inference-examples

Examples for using ONNX Runtime for machine learning inferencing.

Language:PythonMIT97500

PixArt-sigma

PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Language:PythonAGPL-3.0111300

InstantMesh

InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

Language:PythonApache-2.0233800

AniPortrait

AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation

Language:PythonApache-2.0405400

upar_dataset

Official repository of the UPAR dataset for pedestrian attribute recognition and attribute-based person retrieval

Language:Python900

grok-1

Grok open release

Language:PythonApache-2.04890600

xai

XAI - An eXplainability toolbox for machine learning

Language:PythonMIT106800

Website-Image-Scraper

A library to scrape google images

Language:Python2900

sentiment-predictor-for-stress-detection

Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.

Language:Jupyter Notebook8000