miraigajettolab

AirBand

Using OpenCV to create a virtual set of drums and a xylophone

alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

Asm_Learn

Simple x86 assembly examples

bites

docker-qemu-arm

dwm

dwm fork

imgproc

Examples of image processing using kernels

JobHackWeb

https://vk.com/services?w=app7602547_133227497

miraigajettolab/AirBand

Using OpenCV to create a virtual set of drums and a xylophone

miraigajettolab/alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

miraigajettolab/Asm_Learn

Simple x86 assembly examples

miraigajettolab/bites

miraigajettolab/docker-qemu-arm

miraigajettolab/dwm

dwm fork

miraigajettolab/imgproc

Examples of image processing using kernels

miraigajettolab/JobHackWeb

https://vk.com/services?w=app7602547_133227497

miraigajettolab/kalidokit

Blendshape and kinematics solver for Mediapipe/Tensorflow.js face, eyes, pose, and hand tracking models.

miraigajettolab/kikana

Modification of WanaKana that supports Cyrillic script

miraigajettolab/mpv-takeSsSequence

Take a sequence of equispaced screenshots. You can configure the number of screenshots and the video fragment to which it will be applied (you can configure 100% of the video).

miraigajettolab/MSPN

Multi-Stage Pose Network

miraigajettolab/nvim-config

miraigajettolab/pointblank

https://acid-heat.surge.sh

miraigajettolab/tgllm_bot

Build telergam bots with openai LLMs

miraigajettolab/transformers

Code and models for BERT on STILTs

miraigajettolab/UniPose

We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.

miraigajettolab/yt-dlp

A youtube-dl fork with additional features and fixes