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Amaris Consulting
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- https://federicozappone.github.io
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AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Rich is a Python library for rich text and beautiful formatting in the terminal.
Datasets, Transforms and Models specific to Computer Vision
Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
pix2tex: Using a ViT to convert images of equations into LaTeX code.
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
An Open-source Framework for Data-centric, Self-evolving Autonomous Language Agents
A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF
Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model
Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Gin provides a lightweight configuration framework for Python
Simple real time visualisation of the execution of a Python program.
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two
[CVPR'22] NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
TRI-ML Monocular Depth Estimation Repository
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields. https://arxiv.org/abs/2210.13641 + Sigma-Fusion: Probabilistic Volumetric Fusion for Dense Monocular SLAM https://arxiv.org/ab…
This codebase demonstrates how to synthesize realistic 3D character animations given an arbitrary speech signal and a static character mesh.
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
Implementation of the Wave-U-Net for audio source separation
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
PlaneRCNN detects and reconstructs piece-wise planar surfaces from a single RGB image
Selenium-automated Jupyter Notebook that is synchronised with NeoVim in real-time.
Learning audio concepts from natural language supervision
Model Predictive Path Integral (MPPI) with approximate dynamics implemented in pytorch