642 skills found · Page 11 of 22
SincereCSL / Learn GitLearn to use github
codershiyar / GitGit and GitHub Crash Course 2023 - اقوى دورة تعلم جيت و جيت هاب
wafarifki / JungleDevs LandingPageHi there 👋 i want to share my exploration for Jungle Devs Company. Jungle Devs is a Brazilian company focused on developing people who develop software. Use this for learn. Give me your stars, Don't forget to follow my github profile. Thank you >_<
Phonbopit / Solidity Course JsLearn from this repo : https://github.com/smartcontractkit/full-blockchain-solidity-course-js
heyhooman / 0xDSAThis Github repository is dedicated to the study and implementation of various data structures and algorithms Whether you are a beginner looking to learn the basics or an experienced programmer seeking to improve your skills, this repository has something for everyone. So come join us and dive into the world of data structures and algorithms!.
CunjunWang / Emos Service在线协同办公微信小程序后端项目. 相应的前端项目地址: https://github.com/CunjunWang/emos-web. 基于课程https://coding.imooc.com/learn/list/485.html 源代码做了少量重构.
LiuYuancheng / Raspberry PI OPTEEThis project is aimed to create a trustClient(use TrustZone on ARM) and a server program to verify whether a executable program on Raspberry PI. For the part to set the OPTEE on Raspberry PI, We follow benhaz1024's project[https://github.com/benhaz1024/raspbian-tee] to learn & implement a trust APP.
aleepsy / Learn Live GitHub UniverseCrea READMEs impresionantes con Markdown
cobanov / Team Cobanovlearn how to pull request on github
18835596648 / Learn Vue Source CodeVue.js源码学习笔记在线地址https://nlrx-wjc.github.io/Learn-Vue-Source-Code/
deep-learning-su / Deep Learning Su.github.ioNo description available
simranjeet97 / DSA Complete Pattern Recognition GuideThis Github Repository helps you learn and remember the DSA patterns more effectively.
equationl / GithubAppByComposeA Github APP by Jetpack Compose, It's a good project to learn compose and also a good github app
artis3n / Course Vault Github OidcTake this course to learn how to create fine-grained, least-privilege HashiCorp Vault roles for GitHub Action workflows using GitHub OIDC.
fm4dd / Gatemate RiscvRISCV CPU implementation tutorial steps for Cologne Chip Gatemate E1, adopted from https://github.com/BrunoLevy/learn-fpga
Divya4242 / React Node MongoDB Docker Project CICD GITHUB ACTIONS EC2In this guide, you'll learn about the CI/CD pipeline using GITHUB ACTIONS to deploy a React frontend and Node.js backend project onto an AWS EC2 instance. The CI/CD workflow utilizes a github managed runner for GITHUB ACTIONS to facilitate automated deployment. The EC2 instance is pre-installed with Node.js and Nginx.
ajaybhatiya1234 / DEEP FACE Dectection01 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── base_deepfake (1) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── both_deepfake (3) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │ ├── DeepFakeDetection │ ├── Deepfakes │ ├── Face2Face │ ├── FaceSwap │ └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.
selfteaching-learning-notes / Selfteaching Learning Notes.github.io自学营学员学习笔记
learn2reg / Learn2reg.github.ioNo description available
mitre / Saf Training Lab EnvironmentThe SAF Training Lab is a GitHub Codespaces environment that makes it quick and easy for you to use, learn and participate in the MITRE Security Automation Framework Training Classes.
