Pinned Repositories
0-1-Knapsack-Problem-Using-Genetic-Algorithm
Awesome-Physics-Learning
:comet: Collection of the most awesome Physics learning resources in the form of notes, videos and cheatsheets.
Gated_Convolution_Tensorflow
A TensorFlow implementation of Free-Form Image Inpainting with Gated Convolution (https://arxiv.org/abs/1806.03589)
Global-and-Local-Attention-Based-Free-Form-Image-Inpainting
Official implementation of "Global and local attention-based free-form image inpainting"
Image-Quality-Evaluation-Metrics
Implementation of Common Image Evaluation Metrics by Sayed Nadim (sayednadim.github.io). The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.
Inpainting-Evaluation-Metrics
The goal of this repo is to provide a common evaluation script for image inpainting tasks. It contains some commonly used image quality metrics for inpainting (e.g., L1, L2, SSIM, PSNR and LPIPS).
Levin_Colorization_Python
http://webee.technion.ac.il/people/anat.levin/papers/colorization-siggraph04.pdf
Optimization_Thoery_Final_Project
RISP_ECCVW
SIFNet
SayedNadim's Repositories
SayedNadim/Global-and-Local-Attention-Based-Free-Form-Image-Inpainting
Official implementation of "Global and local attention-based free-form image inpainting"
SayedNadim/SIFNet
SayedNadim/RISP_ECCVW
SayedNadim/json
JSON for Modern C++
SayedNadim/ML-inference-baseline
SayedNadim/awesome-public-datasets
A topic-centric list of HQ open datasets.
SayedNadim/camera-utils
SayedNadim/DAGF
Deep Attentional Guided Image Filtering, Winner solution for ICMR 2021 Real DSR Challenge (IEEE TNNLS 2023)
SayedNadim/Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
SayedNadim/DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
SayedNadim/DeepUnrollNet
Deep Shutter Unrolling Network
SayedNadim/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
SayedNadim/hdr-plus-pytorch
A PyTorch implementation of HDR+ with GPU support.
SayedNadim/JAMNet
The source code for the paper: Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction (CVPR2023)
SayedNadim/mediapipe
Cross-platform, customizable ML solutions for live and streaming media.
SayedNadim/OURS-project
Step-by-step instructions to build a smartphone that is open-source, upgradeable, repairable, and Big Tech free.
SayedNadim/Pseudo-LiDARs-with-Stereo-Vision
This project focuses on harnessing the power of Pseudo-LiDARs and 3D computer vision for unmanned aerial vehicles (UAVs). By integrating Pseudo-LiDAR technology with Stereo Global Matching (SGBM) algorithms, we aim to enable UAVs to perceive their surroundings in three dimensions accurately.
SayedNadim/python_stereo_camera_calibrate
Stereo camera calibration with python and openCV
SayedNadim/PythonRobotics
Python sample codes for robotics algorithms.
SayedNadim/pytorch-cifar
95.47% on CIFAR10 with PyTorch
SayedNadim/pytorch-complex
SayedNadim/ROMP
ROMP: Monocular, One-stage, Regression of Multiple 3D People, ICCV21
SayedNadim/rpg_ramnet
Code and datasets for the paper "Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction" (RA-L, 2021)
SayedNadim/sayednadim
SayedNadim/sayednadim.github.io
A beautiful, simple, clean, and responsive Jekyll theme for academics
SayedNadim/sharpened-cosine-similarity
An alternative to convolution in neural networks
SayedNadim/surround-view-system-introduction
SayedNadim/terminal-based-deadline-management
SayedNadim/the-incredible-pytorch
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
SayedNadim/tonic
Publicly available event datasets and transforms.