ivanvoid's Stars
astral-sh/uv
An extremely fast Python package and project manager, written in Rust.
ultralytics/ultralytics
Ultralytics YOLO11 🚀
microsoft/AI-For-Beginners
12 Weeks, 24 Lessons, AI for All!
huggingface/diffusers
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
facebookresearch/sam2
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
hoffstadt/DearPyGui
Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies
openai/shap-e
Generate 3D objects conditioned on text or images
marcotcr/lime
Lime: Explaining the predictions of any machine learning classifier
keylase/nvidia-patch
This patch removes restriction on maximum number of simultaneous NVENC video encoding sessions imposed by Nvidia to consumer-grade GPUs.
ashawkey/torch-ngp
A pytorch CUDA extension implementation of instant-ngp (sdf and nerf), with a GUI.
bghira/SimpleTuner
A general fine-tuning kit geared toward diffusion models.
MaximeVandegar/Papers-in-100-Lines-of-Code
Implementation of papers in 100 lines of code.
Stability-AI/stable-fast-3d
SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement
tancik/fourier-feature-networks
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
yashbhalgat/HashNeRF-pytorch
Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives: https://nvlabs.github.io/instant-ngp/
davidfraser/pyan
pyan is a Python module that performs static analysis of Python code to determine a call dependency graph between functions and methods. This is different from running the code and seeing which functions are called and how often; there are various tools that will generate a call graph in that way, usually using debugger or profiling trace hooks - for example: https://pycallgraph.readthedocs.org/ This code was originally written by Edmund Horner, and then modified by Juha Jeronen. See README for the original blog posts and links to their repositories.
OniroAI/MonoDepth-PyTorch
Unofficial implementation of Unsupervised Monocular Depth Estimation neural network MonoDepth in PyTorch
POSTECH-CVLab/SCNeRF
[ICCV21] Self-Calibrating Neural Radiance Fields
wbhu/Tri-MipRF
Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields, ICCV'23 (Oral, Best Paper Finalist)
thodan/bop_toolkit
A Python toolkit of the BOP benchmark for 6D object pose estimation.
facebookresearch/robust-dynrf
An algorithm for reconstructing the radiance field of a dynamic scene from a casually-captured video.
ChikaYan/d2nerf
fpingham/google-images-dataset
This repository provides the necessary code to create your own Google Images Dataset.
alinstein/Depth_estimation
Deep learning model to estimate the depth of image.
houchenst/FastNeRF
Ending2015a/hash-grid-encoding
Pure PyTorch implementation of Nvidia's hash grid encoding: https://nvlabs.github.io/instant-ngp/
azad-academy/stable-diffusion-model-tutorial
An easy guide to stable diffusion models.
JokerYan/NeRF-DS
This is the code for "NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects".
Jeffrey-Ede/datasets
Visualization of electron microscopy datasets with deep learning
Hanlin-Zhou/PyTorch-InstantNGP
PyTorch Implementation of Instant-NGP