dsvolkov

dsvolkov's Stars

• donnemartin/system-design-primer

  Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

  Language:Python286k6.7k31647.7k

• github/gitignore

  A collection of useful .gitignore templates

  164k3.4k083.1k

• huggingface/pytorch-image-models

  The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

  Language:Python32.9k3189514.8k

• hoya012/deep_learning_object_detection

  A paper list of object detection using deep learning.

  Language:Python11.3k622272.8k

• dusty-nv/jetson-inference

  Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

  Language:C++8.1k2731.8k3k

• automl/auto-sklearn

  Automated Machine Learning with scikit-learn

  Language:Python7.7k2141k1.3k

• kba/awesome-ocr

  Links to awesome OCR projects

  2.9k12870354

• strawlab/python-pcl

  Python bindings to the pointcloud library (pcl)

  Language:Cython2k56324700

• r1chardj0n3s/parse

  Parse strings using a specification based on the Python format() syntax.

  Language:Python1.7k25112102

• brektrou/rtl8821CU

  Realtek RTL8811CU/RTL8821CU USB Wi-Fi adapter driver for Linux

  Language:C1.6k43174471

• lehaifeng/T-GCN

  Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

  Language:Jupyter Notebook1.5k2397453

• filipradenovic/cnnimageretrieval-pytorch

  CNN Image Retrieval in PyTorch: Training and evaluating CNNs for Image Retrieval in PyTorch

  Language:Python1.4k3376323

• Celebrandil/CudaSift

  A CUDA implementation of SIFT for NVidia GPUs (1.2 ms on a GTX 1060)

  Language:Cuda8814185287

• the-black-knight-01/Data-Science-Competitions

  Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).

  801540217

• erget/StereoVision

  Library and utilities for 3d reconstruction from stereo cameras.

  Language:Python6584537267

• spMohanty/PlantVillage-Dataset

  Dataset of diseased plant leaf images and corresponding labels

  Language:Python6583515675

• AndrewAnnex/SpiceyPy

  SpiceyPy: a Pythonic Wrapper for the SPICE Toolkit.

  Language:Python4062815483

• ybabakhin/kaggle_salt_bes_phalanx

  Winning solution for the Kaggle TGS Salt Identification Challenge.

  Language:Python35310784

• andyljones/coolgpus

  GPU fan control for headless Linux

  Language:Python33781746

• DOI-USGS/ISIS3

  Integrated Software for Imagers and Spectrometers v3. ISIS3 is a digital image processing software package to manipulate imagery collected by current and past NASA and International planetary missions.

  Language:C++202303.5k171

• lquirosd/P2PaLA

  Page to PAGE Layout Analysis Tool

  Language:Python191143742

• SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions

  Winning algorithms for the SpaceNet 4: Off-Nadir Building Footprint Detection Challenge

  Language:Python17311436

• HazyResearch/TreeStructure

  Table Extraction Tool

  Language:Jupyter Notebook9133235

• heigeo/climata

  Python library for loading and iterating over climate and flow time series data (from ACIS/NOAA RCCs, CoCoRaHS, Hydromet/USBR, CNRFC ESP/NWS, SNOTEL/AWDB/NRCS, and NWIS/USGS)

  Language:Python85191014

• ThinkPadThink/Guide2011-3

  Гайд по сокету LGA2011-3 и в целом по платформе Intel X99

  68936

• SETI/rms-tools

  Language:Python3315816

• georg-un/digipathos-plant-disease-img-db-downloader

  Script to download the Digipathos plant disease image database from the EMBRAPA website

  Language:Python8101

• vbookshelf/Apple-Leaf-Health-Analyzer

  Ai powered web app to automatically analyze the health of apple tree leaves.

  Language:Jupyter Notebook520

• namakemono/kaggle-cookbook

  過去コンペの上位陣解法や役立つカーネルまとめ(勉強用)

  40551

• sprinklebum/iMars-eu

  The iMars project focuses on developing tools and value-added datasets to massively increase the exploitation of space-based data from NASA and ESA mission imaging and 3D data beyond the PI teams. iMars is adding value by creating more complete and fused 3D models of the surface from combined stereo and laser altimetry and using these 3D models to create a set of co-registered imaging data through time, permitting a much more comprehensive interpretation of the Martian surface to be made.

  Language:Shell1201