this repo consists of several challenge ideas for a CTF, all based around web challenges. All can be run in docker containers, where the git and cert challenge run in the same container (docker-cert-and-git-poc). The first challenge consists of a “sql based cookie injection”, using flask as a webserver and mysql as the database. challenge 1 The second challenge resolves around git. challenge 2 The third challenge is about authenticating with a client cert with a certain “Subject” field in […]

TLDR: One byte write, no leak. Infinite loop through overwrite of binaries link map Determination of useful rop gadget, has to fit several extensive criteria Partial overwrite of DT_JMPREL table pointer Partial overwrite of DT_STRTAB pointer Overwrite of LIBC link map Loop 2-5 until rop chain has been created Partial overwrite of DT_FINI_ARRAYSZ pointer Use 3 & 4 to call exit, to call rop chain Prologue This challenge was extremely difficult and I recommend reading the original author’s writeup to […]

Simulator for FRC 2022 challenge: Rapid React out-1.mp4 Usage In order to run the simulator use the following: python3 rrsim.py [config_path] where config_path is the path to the json configuration (default value is default_configs/config.json). Configurations In order to configure game, field and robots, a config JSON file must be created. See default_configs directory for examples of configurations. The following are parameters that can be defined in the configuration: Per-robot parameters: Name Type Meaning Example starting_position Tuple[float, float] Starting position of […]

This is the pytorch implementation for UDP++, which won the Fisrt place in COCO Keypoint Challenge at ECCV 2020 Workshop. Results on MPII val dataset Method— Head Sho. Elb. Wri. Hip Kne. Ank. Mean Mean 0.1 HRNet32 97.1 95.9 90.3 86.5 89.1 87.1 83.3 90.3 37.7 +Dark 97.2 95.9 91.2 86.7 89.7 86.7 84.0 90.6 42.0 +UDP 97.4 96.0 91.0 86.5 89.1 86.6 83.3 90.4 42.1 Results on COCO val2017 with detector having human AP of 65.1 on COCO val2017 […]

pipeline: Chuẩn bị dữ liệu cho quá trình train: python preprocessing.py python utils/split_train_val_by_id.py python utils/augment_mp3.py python utils/preprocess_augment.py Train model: python convert_data.py python train.py Infer public test: Đặt dữ liệu mp3 thô ở địa chỉ /data/public_test (bên trong chứa 2 thư mục full_song và hum) Chạy lần lượt các lệnh sau: ./predict.sh Infer private test: Đặt dữ liệu mp3    

Overview Yolov5, Yolor, mmdetection기반의 모델을 사용 (총 11개 모델 앙상블) 모든 모델은 학습 시 Pretrained Weight을 yolov5, yolor, mmdetection 및 swin transformer github로부터 받아서 사용 각 방식에 필요한 형태로 데이터의 format 변경 Train set과 Validation set을 나누어 진행 총 11개의 결과를 앙상블 detectors_casacde_rcnn_resnet50_multiscale, retinanet_swin-l, retinanet_swin-l_multiscale, retinanet_swin-t, atss_swin-l_multiscale, faster_rcnn-swin-l_multiscale, yolor_tta_multiscale, yolov5x, yolov5x_tta, yolov5x_tta_multiscale Weighted boxes fusion (WBF) 방식으로 앙상블 진행 (Iou threshold = 0.4) 모델에 관한 보다 자세한 내용은 /all_steps 폴더 내에 STEP2로 시작하는 .sh 스크립트들에 적힌 주석을 참고해주세요! 환경(env) 세팅 […]

This repository contains my solutions for the 2021 edition of Advent of Code. Please do not expect perfectly polished solutions, most scripts are just hacked together and never touched again. Same holds for tests, as most of my solutions are developed using REPL Driven Development instead of Test Driven Development. 🙂 Overview Stars so far: 8/8 Setup If you want to reproduce my solutions using your own input (no guarantees my hacky code will work for your inputs!), dependencies are […]

English Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. Each challenge requires thinking of a way to solve it and it´s interesting to look at how others solve them with different languages and ideas. I’ve chosen Python as programming language to solve the puzzles. I’ll upload the solution for each puzzle of the day to the repository. Español […]

This repository supports the L3DAS22 IEEE ICASSP Grand Challenge and it is aimed at downloading the dataset, pre-processing the sound files and the metadata, training and evaluating the baseline models and validating the final results.We provide easy-to-use instruction to produce the results included in our paper.Moreover, we extensively commented our code for easy customization. For further information please refer to the challenge website and to the challenge documentation. Installation Our code is based on Python 3.7. To install all required […]

😉😉😉😉😉Here You will Find CodeChef Challenge Solutions You can Re Use My Code , and If It Helped You Just Drop A thumbs Up 👍👍On any of My Socials … Instagram – https://www.instagram.com/kanishk.iot/ Mail – [email protected] Happy Coding. GitHub View Github