Author: Deep Learner

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Unlocking new dimensions in image-generation research with Manifold Matching via Metric Learning

Generative image models offer a unique value by creating new images. Such images can be sharp super-resolution versions of existing images or even realistic-looking synthetic photographs. Generative Adversarial Networks (GANs) and their variants have demonstrated pioneering success with the framework of training two networks against each other: a generator network learns to generate realistic fake data that can trick a discriminator network, and the discriminator network learns to correctly tell apart the generated fake data from the real data. In […]

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Fusion 360

Fusion 360 Write UserInterface For Python

This is a far more in-depth and advanced version of “Write user interface to a file API Sample” from https://help.autodesk.com/view/fusion360/ENU/?guid=GUID-d2b85a7e-fd08-11e4-9e07-3417ebd3d5be Quick warning, if you choose to have the full indepth file created, prepare for a file containing about 30,000 lines of text 🙂 (The minimal version is only about 6,000 lines) this script literally parses the entire fusion ui in a second or two including all controls, tabs, panels, workspaces, and command definitions so every command fusion has is included […]

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Email

A repo by Lukas Schubotz and Raymon van Dinter

We’re the hackathon leftovers, but we are Too Good To Go ;-). A repo by Lukas Schubotz, Stef van Buuren, and Raymon van Dinter. We aim to improve current data preprocessing for FTM’s WOB data to analyze Shell and Dutch Governmental contacts. Synchronous visualisation of email threads Publications from the FTM “Dossier SHELL papers” https://www.ftm.nl/dossier/shell-papers suggest that timing of events is critical in the interactions between actors. It would therefore be useful if we could visualise the mail exchanges in […]

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Tool

A Python Tool that uses Shodan API’s to perform quick recon for vulnerabilities

A Python Tool that uses Shodan API’s to perform quick recon for vulnerabilities You must edit the python code, and insert your Shodan API Where it is stated & Save it. The word apache is already definted, and will extract whatever you change it to, so each time you can setup a word to perform the search, and you can save the output results using > output.txt in order to cat/work and grep things of interest. More info on tweaking […]

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Scripts

Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX

Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX Stereo depth estimation on the cones images from the Middlebury dataset (https://vision.middlebury.edu/stereo/data/scenes2003/) Check the requirements.txt file. Additionally, pafy and youtube-dl are required for youtube video inference. DrivingStereo dataset, ONLY for the driving_sereo_test.pyscript. Link: https://drivingstereo-dataset.github.io/ pip install -r requirements.txt pip install pafy youtube-dl The original models were converted to different formats (including .onnx) by PINTO0309, the models can be found in his repository. The Pytorch pretrained model was […]

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Controller

A utility control surface for Ableton Live that makes the initialization of a Mixdown quick

A script that transfers all the VSTs on your MIDI tracks to a new track so you can freeze your MIDI tracks and then copies the VSTs back over for you and deletes the new tracks Setup: Copy paste the downloaded/cloned repository in your Remote Scripts folder. Refer to the link to find out where the folder is for your OS:https://help.ableton.com/hc/en-us/articles/206240184-Creating-your-own-Control-Surface-script Select “ableton-mixdown-automation” in Preferences > Link/MIDI Go to: 127.0.0.1:5010 or localhost:5010 in your browser and follow the instructions Note […]

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Hash

This program generate hashes from random salts

This program generate hashes from random salts. How to install Install this program using python 3 and pip: pip install . In the future, this program will be available for linux and macOS using the most common package managers. How to use The command to use this tools is pg. There is some options: list: get a list of algorithms allowed generate: generate a bcrypt algorithm by default How to use an specific algorithm There is some flags for the […]

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Task

Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks

This repository contains the official code for the paper Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks. Requirements This codebase has been tested with the following package versions: python=3.8.8 torch=1.9.0+cu102 torchvision=0.10.0+cu102 PIL=8.1.0 numpy=1.19.2 scipy=1.6.1 tqdm=4.57.0 sklearn=0.24.1 albumentations=1.0.3 Prepare data There are several classes defined in the datasets directory. The data is expected in a directory name data, located on the same level as this repository. Below is an outline of the expected file structure: data/ […]

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