403 skills found · Page 5 of 14
jplassonde / Image2svgShort python script to convert images to straight vectors line art, to be printed on an XY plotter.
datruccone / PortfolioEvolutionR Script that import your daily Portfolio export from DeGiro, then save it in Excel, compare your performance with mayor Indexes and plot
lanl / LAVALava is a general-purpose calculator that provides a python interface to enable one-click calculation of the many common properties with LAMMPS and VASP. The name Lava is derived from the “La” in LAMMPS and “va” in VASP. It provides a set of classes and functions to generate configurations, run lammps/vasp calculation, retrieve the output, postprocess and plot the results. All the above tasks are hard-coded into the script, without the need to call additional libraries.
K4ys4r / BoltzGnuBoltzGnu Contains Gnuplot Scripts which allow to plot BoltzTraP Output Data
C2SM / Icon VisCollection of Python scripts and notebooks to demonstrate plotting on the ICON grid.
giopaglia / RoofliniA Python script for plotting roofline analyses. Intel Advisor style.
smousavi05 / GMT ScriptsThese are some Shell scripts used for generating high-quality and professional plots using GMT.
scikit-learn / Sklearn DocbuilderScript to configure a cloud server to build the documentation and plots and update the sklearn website
boivinalex / PermittivitycalcScripts to calculate and plot the complex permittivity from S-parameter data
OH6BG / VOACAP MAPSScripts to run VOACAP P2P prediction matrix, plot maps and store to database
cooplab / Genetic AncestorsScripts to generate simulations and plots used in blog posts on genetic and genealogical ancestry gcbias.org/category/genetic-genealogy/ . These scripts were written over a couple of afternoons and are by no means optimal ways of constructing these quantities. Hopefully they will be of use.
lrkrol / Plot ErpScript to plot and analyse ERPs from EEGLAB datasets.
wardahfadil / TectoplotGMT based plotting script for seismotectonics
jvde-github / Ais2adsbSimple script to convert AIS NMEA to ADSB basestation format. Main purpose is to plot SAR helicopters in ADSB plotting software like VRS.
ThomasDebrunner / Bode.tnsA bode plot script for TI-Nspire written in lua
notha99y / TLEPython script that can plot a given satellite's TLE and train a linear regression model to predict future satellite's state vector.
andreamanzini / Acustic OFDMThis project uses the speaker and the microphone of the computer to realize a complete acoustic OFDM communication system in the MATLAB environment. The system includes phase estimation, channel equalization, bits encoding and cyclic prefix. The main script transmits an example image and shows the received one. Some plots are automatically generated to evaluate the system performances.
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.
Ossus / AppannieScripts to use AppAnnie's API to download comments and sales data, write to CSV and plot using R
earthinversion / GMT Tutorial For BeginnersThis package contains the scripts and commands to plot all types of high resolution figures using Generic Mapping Tools (GMT)
