gokriznastic's Stars
Developer-Y/cs-video-courses
List of Computer Science courses with video lectures.
Textualize/rich
Rich is a Python library for rich text and beautiful formatting in the terminal.
PeterL1n/RobustVideoMatting
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
brycedrennan/imaginAIry
Pythonic AI generation of images and videos
replicate/cog
Containers for machine learning
huggingface/accelerate
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
facebookresearch/SlowFast
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
facebookresearch/ConvNeXt
Code release for ConvNeXt model
facebookresearch/DiT
Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
nianticlabs/monodepth2
[ICCV 2019] Monocular depth estimation from a single image
huggingface/deep-rl-class
This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.
pytorch/TensorRT
PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
lucidrains/lion-pytorch
🦁 Lion, new optimizer discovered by Google Brain using genetic algorithms that is purportedly better than Adam(w), in Pytorch
zengyh1900/Awesome-Image-Inpainting
A curated list of image inpainting and video inpainting papers and resources
ialhashim/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
lukasschwab/arxiv.py
Python wrapper for the arXiv API
PyGCL/PyGCL
PyGCL: A PyTorch Library for Graph Contrastive Learning
wyhsirius/LIA
[ICLR 22] Latent Image Animator: Learning to Animate Images via Latent Space Navigation
facebookresearch/ic_gan
Official repository for the paper "Instance-Conditioned GAN" by Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.
facebookresearch/eft
visualization code for 3D human body annotation by EFT (Exemplar Fine-tuning)
manideep2510/eye-in-the-sky
Satellite Image Classification using semantic segmentation methods in deep learning
yukimasano/PASS
The PASS dataset: pretrained models and how to get the data
ultralytics/xview-yolov3
xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.
YuvalNirkin/hyperseg
HyperSeg - Official PyTorch Implementation
OFA-Sys/OFASys
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
WeilanAnnn/FD-GAN
FD-GAN: Generative adversarial Networks with Fusion-discriminator for Single Image Dehazing(AAAI'20)
nrhinehart/deep_imitative_models
Reimplementation (currently partial) of Deep Imitative Models paper, ICLR '20
facebookresearch/3D-Vision-and-Touch
When told to understand the shape of a new object, the most instinctual approach is to pick it up and inspect it with your hand and eyes in tandem. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines, especially when the object is occluded by the hand touching it; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) reconstruction quality boosts with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.
BY571/Implicit-Q-Learning
PyTorch implementation of the implicit Q-learning algorithm (IQL)
amazon-science/network-deconvolution-pp