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tmbdev-forks's Projects

ai-art icon ai-art

PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!

bcnc icon bcnc

GRBL CNC command sender, autoleveler and g-code editor

dnote icon dnote

A simple command line notebook for programmers

doc3d-renderer icon doc3d-renderer

Blender rendering codes for doc3D-dataset (Paper: https://www3.cs.stonybrook.edu/~cvl/projects/dewarpnet/storage/paper.pdf)

docmae icon docmae

Unofficial implementation of DocMAE (WIP): Document Image Rectification via Self-supervised Representation Learning

doctr icon doctr

docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

flag icon flag

Flag is a simple but powerful command line option parsing library for Go support infinite level subcommand

flan-alpaca icon flan-alpaca

This repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5.

go-arg icon go-arg

Struct-based argument parsing in Go

goreleaser icon goreleaser

Deliver Go binaries as fast and easily as possible

gotty icon gotty

Share your terminal as a web application

lotus icon lotus

Implementation of the Filecoin protocol, written in Go

mangos icon mangos

mangos is a pure Golang implementation of nanomsg's "Scalablilty Protocols"

msgpack icon msgpack

easier, faster, but extendable MessagePack Serializer for Golang. / msgpack.org[Go]

nanogpt icon nanogpt

The simplest, fastest repository for training/finetuning medium-sized GPTs.

nng icon nng

nanomsg-next-generation -- light-weight brokerless messaging

pdftotree icon pdftotree

:evergreen_tree: A tool for converting PDF into hOCR with text, tables, and figures being recognized and preserved.

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-lightning icon pytorch-lightning

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

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