29 skills found
m1k1o / Go TranscodeOn-demand transcoding origin server for live inputs and static files in Go using ffmpeg. Also with NVIDIA GPU hardware acceleration.
xiaoyeshenger / Java Pytorch Onnx Tensorrt Yolov8 Vue基于SpringBoot+Docker+Cuda+Cudnn+Pythorch+Onnx+Tensorrt+Yolov8+ffmpeg+zlmediakit 的AI算法中台系统，本系统主要实现JAVA调用Python脚本的方式，实现在英伟达GPU上进行yolov8的onnx或tensorrt的推理运算，实现火灾烟雾等视频的识别，并将识别结果通过ffmpeg推流到zlmediakit流媒体服务器。
toki-plus / AB Video Deduplicator一款强大的Python视频去重GUI工具，采用高帧率抽帧混合算法，以规避短视频平台查重。支持GPU加速。video-processing, automation, tiktok, selenium, pyqt5, ffmpeg, bot, data-scraping, video-deduplication.
hostsoft / Ffmpegtoolkitffmpeg auto installer scripts
m1k1o / Hls RestreamRestream live content as HLS using ffmpeg in docker. Also with NVIDIA GPU hardware acceleration.
intel / Ffmpeg LibyamiEnable FFmpeg to use Libyami/VAAPI for hardware offload of video transcode on the Intel GPU
CloudTranscoding / CTSphp ffmpeg transcoding system
myoshimi / Ffmpeg NvencDockerfile to execute ffmpeg including HW acceleration by GPU(nvenc)
zshnb / Ffmpeg Gpu Compile GuideA guide with shell script can quickly help you to compile latest ffmpeg with Nvidia CUDA
iliul / Ffmpeg GpuFFmpeg 4.0 with NVIDIA P4 GPU Driver Support
adilinden-oss / Docker Rpi FfmpegFFmpeg with the OpenMAX driver, which allows use of the Raspberry Pi GPU for H.264 encoding
juniorxsound / Now📼 A slim FFMPEG wrapper to easily transcode videos to multiple resolutions using Nvidia GPUs
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.
instillai / Extract Audio From Video GpuExtracting audio from video using GPU-accelerated FFMPEG
rvs76 / StaxripModern x64 video encoding GUI for many powerful x64 video tools such as AviSynth+, VapourSynth, mkvtoolnix, x264, x265, Intel and NVIDIA GPU encoding, eac3to, ffmpeg and many more. All tools and more then 20 AviSynth+ or VapourSynth plugins are included.
moebiussurfing / OfxSurfingCapturer DEPRECATEDopenFrameworks addon workflow helper for window capture using fast and threaded saving per-frame-stills (.tif), and FFmpeg GPU encoding (.mp4) after using command script.
theodorecharles / BroadcasterCreate your own 24/7 TV channels from your media library with a retro CRT-style web interface. Broadcaster generates and hosts HTTP Live Streams from local content using FFMpeg with NVIDIA GPU hardware acceleration.
seethroughlab / LibnclcOpen source NotchLC video codec library — decoder, GPU path, encoder, FFmpeg integration
proemergotech / Nginx Rtmp OpentracingA Dockerfile installing Openresty based Nginx with the default Openresty modules, nginx-rtmp-module, Nginx Opentracing module with Jaeger tracer and FFmpeg from source with default settings for HLS live streaming and nvidia GPU support.
Brainiarc7 / Ffmpeg Nvenc Windows Build With MxeThis is a Windows-only build of FFMpeg that you can cross-compile on Linux with mxe toolkit. This build supports NVIDIA NVENC hardware encoder on Kepler and Maxwell GPUs.
