Karan-code3108's Stars
codecrafters-io/build-your-own-x
Master programming by recreating your favorite technologies from scratch.
evmos/evmos
Evmos is the canonical EVM chain on Cosmos. Evmos is the flagship implementation of evmOS, a stack to build forward compatible EVMs
CosmosContracts/juno
Open Source Platform for Interoperable Smart Contracts
informalsystems/basecoin-rs
An example ABCI application making use of tendermint-rs and ibc-rs
blockchainworkers/conch
a blockchain project based pos+pbft consensus algorithm and evm powered by tendermint and ethereum
datachainlab/hypermint
Tendermint-based blockchain that supports WebAssembly smart contract
AlvaroCavalcante/auto_annotate
Labeling is boring. Use this tool to speed up your next object detection project!
donnemartin/system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
PierfrancescoSoffritti/RemoteVR_UnityServer
Multiplayer virtual reality in the cloud. (Unity server)
paxosglobal/usdp-contracts
Solidity smart contracts for the Paxos Standard ERC20 stablecoin USDP
paxosglobal/paxos-gold-contract
SadilKhan/AutoLabelMe
Open Source Automatic Image Annotator in Python
robertarvind/Interactive-Semi-Automatic-Image-2D-Bounding-Box-Annotation-Tool-using-Multi-Template_Matching
Interactive Semi Automatic Image 2D Bounding Box Annotation and Labelling Tool using Multi Template Matching An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool to aid the Annotater/User to rapidly create 2D Bounding Box Single Object Detection masks for large number of training images in a semi automatic manner in order to train an object detection deep neural network such as Mask R-CNN or U-Net. As the Annotater/User starts annotating/labelling by drawing a bounding box for a few number of images in the selected folder then the algorithm suggests bounding box predictions for the rest of the yet to be annotated/labelled images in the folder. If the predictions are right then the user/annotater can simply press the keyboard key 'y' which indicates that the detected bounding box is correct. If the prediction is wrong then the user/annotater can manually draw a rectangular 2D bounding box over the correct ROI (Region of interest) in the image and then press the key 'y' to proceed further to the rest of the images in the folder. If the user/annotater made a mistake while drawing the 2D bounding box, then he/she can press the key 'n' in order to remove the incorrectly marked 2D bounding box and he/she can repeat the process for the same image until he/she draws the correct 2D bounding box and then after drawing the correct 2D bounding box, the user/annotater may press the key 'y' to continue to the rest of the images. The 2D bounding box prediction over the whole image data set improves as the user/annotater annotates/labels more number of images by drawing 2D bounding boxes. This tool allows the user/annotater to not only interactively and rapidly annotate large number of images but also to validate the predictions at the same time interactively. This tool helps the user/annotater to save a lot of time when annotating/labelling and validating the predictions for a large number of training images in a folder. Instructions to use:- 1. If the training images are in JPEG or any other format, then convert them to PNG format using some other tool or program before using these images for annotation. 2. All the training images must contain the object of interest which is to be annotated. 3. Currently the application only supports 2D bounding box annotation for single object detection per image, but in the future semantic segmentation based annotation features will be added which will allow precise boundary segmentation masks of an object in an image. 4. If some or all of the training images have varying dimensions(shapes/resolutions), then resize them to the same dimensions using this tool by providing the height and width to which all the training images need to be resized to. The height and width are inputed separately in two different dialog boxes which pop up once the program is executed. If the training images need not be resized then press the cancel button in the dialog boxes requesting the height and width. 5. Select the folder containing the training images by navigating to the folder containing the training images through a dialog box which pops up after the program is executed. If the images need to be resized then two dialog boxes pop up. The first dialog box is to navigate to the destination folder containing the unresized raw training images and after resizing another dialog box pops up to navigate to the folder containing the saved resized training images named as "resized_data". If the images need not be resized then only one dialog box pops up so that the user can navigate to the raw training images folder directly. 6. The images in the folder pop up one by one. After drawing the correct 2D bounding box over the ROI (region of Interest), press the 'y' key. Except the first image, the rest of the images will have a 2D bounding box drawn over them. If the predicted box is accurate, then continue by pressing the 'y' key. If the prediction is incorrect, then draw the accurate bounding box and press the 'y' key. If any mistake occured while drawing the 2D box, then reset the image by removing the incorrect drawing by pressing the 'n' key and then draw the correct box and press the 'y' key. 7. The output images are stored in four different folders in the same directory containing the training images folder. among the four folders, one contains the cropped templates of the bounding boxes, black and white mask images, training images and the images with 2D box detection markings.
annomator/annomator_1.0
Annomator is a fully featured automatic image annotator. It can detect, record, edit and display masks and boxes from objects detected in images.
ofeksadlo/AutoLabelImg
Automatically annotate images using your own pre-trained yolo models.
mdhmz1/Auto-Annotate
Auto-Annotate - Automatically annotate your entire image directory by a single command. As simple as saying - "Annotate all the street sign (label) in the autonomous car dataset (directory)" and BAM! DONE. Each and every image with a street sign in the diverse dataset directory containing images of all sorts which have a street sign are filtered and the segmentation annotation is performed in a single command. The Auto-Annotate tool provides auto annotation of segmentation masks for the objects in the images inside some directory based on the labels. Auto-Annotate is able to provide automated annotations for the labels defined in the COCO Dataset and also supports Custom Labels.
WilsonDhChen/mediasrv_windows
流媒体服务器Windows版本 RTMP HLS RTSP Stream server。 Support HLS RTMP RTSP HTTP-TS HTTP-FLV HTTP-AAC。 Output video in 0.2-0.3 seconds，very fast。
bschackathon/digi_market
Monalisa: NFT based marketplace on Binance SmartChain for digital artwork.
CrescenDoge/NFT-Marketplace
Source code for Crescendo NFT Marketplace, music distribution on Binance Smart Chain.
ervikassingh/nft-staking
A marketplace where one can buy pre-uploaded Graphic NFTs (Shroomies) on IPFS. One can also buy Heros which are randomly minted according to their rarity. Heroes can be staked to earn rewards and have their own marketplace. Contracts are deployed on Binance Smart Chain by default.
misaelvillegas97/CNFT-marketplace
Cardano NFT marketplace
jianhuaixie/blockchain-buildwheels
blockchain project ,build a public chain from zero.
theoturner/Python-Blockchain
Public blockchain and GUI for immutable decentralised data storage.
islantia/Islantia.sol
In-game items are a big part of any video game and allow gamers to own digital assets. However, in-game items are centralized, not easily exchangeable, and challenging to implement. Islantia is a smart contract platform that allows game developers to mint, distribute and transact Smart NFTs representing in-game items. Smart NFTs are NFTs with dynamic properties that can change based on certain conditions. Smart NFTs are the next step to making decentralized in-game items mainstream and offer a massive range of potential opportunities that are not possible with traditional NFTs. We want to create an easy-to-use interface and API for creating Smart NFTs, an open marketplace for transacting NFTs using the Islantia token, and we want to introduce truly random smart loot boxes to the blockchain.
NP-compete/Alternate-Authentication
A project for Smart India Hackathon (World's Biggest Hackathon) 2019
expload/pravda
Blockchain with Turing-complete VM implemented in Scala.
s0sasaki/ExecutableBlockchain
A blockchain influenced by Ethereum, featuring a Forth compiler.
phantasma-io-archive/PhantasmaChain
Blockchain with native storage and smart contract integration.
IBM/blockchainbean2
This code pattern shows how to model a supply-chain network using the IBM Blockchain Platform and is based on a collaboration with Brooklyn Roasting Company. The story, along with the supply-chain documents that were used to model this network, can be found at: https://www.ibm.com/blockchainbean. Note that the 'view the blockchain' button is being migrated''
trustwallet/wallet-core
Cross-platform, cross-blockchain wallet library.