QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Overview

QuickAI logo

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Announcement video https://www.youtube.com/watch?v=kK46sJphjIs

Motivation

When I started to get into more advanced Machine Learning, I started to see how these famous neural network architectures(such as EfficientNet), were doing amazing things. However, when I tried to implement these architectures to problems that I wanted to solve, I realized that it was not super easy to implement and quickly experiment with these architectures. That is where QuickAI came in. It allows for easy experimentation of many model architectures quickly.

Dependencies:

Tensorflow, PyTorch, Sklearn, Matplotlib, Numpy, and Hugging Face Transformers. You should install TensorFlow and PyTorch following the instructions from their respective websites.

Why you should use QuickAI

QuickAI can reduce what would take tens of lines of code into 1-2 lines. This makes fast experimentation very easy and clean. For example, if you wanted to train EfficientNet on your own dataset, you would have to manually write the data loading, preprocessing, model definition and training code, which would be many lines of code. Whereas, with QuickAI, all of these steps happens automatically with just 1-2 lines of code.

The following models are currently supported:

  1. Image Classification

    • EfficientNet B0-B7
    • VGG16
    • VGG19
    • DenseNet121
    • DenseNet169
    • DenseNet201
    • Inception ResNet V2
    • Inception V3
    • MobileNet
    • MobileNet V2
    • MobileNet V3 Small & Large
    • ResNet 101
    • ResNet 101 V2
    • ResNet 152
    • ResNet 152 V2
    • ResNet 50
    • ResNet 50 V2
    • Xception
  2. Natural Language Processing

    • GPT-NEO 125M(Generation, Inference)
    • GPT-NEO 350M(Generation, Inference)
    • GPT-NEO 1.3B(Generation, Inference)
    • GPT-NEO 2.7B(Generation, Inference)
    • Distill BERT Cased(Q&A, Inference and Fine Tuning)
    • Distill BERT Uncased(Named Entity Recognition, Inference)
    • Distil BART (Summarization, Inference)
    • Distill BERT Uncased(Sentiment Analysis & Text/Token Classification, Inference and Fine Tuning)

Installation

pip install quickAI

How to use

Please see the examples folder for details.

Issues/Questions

If you encounter any bugs, please open a new issue so they can be corrected. If you have general questions, please use the discussion section.

Comments
  • Memory error

    Memory error

    Is it possible to host the gpt neo models on a website and make some kind of API, the models are to large to run on my computer. Also It would be nice if to have a stop function so the model knows at what token to stop and be able to add examples of the query needed.

    enhancement 
    opened by TheProtaganist 5
  • Add link to a demo

    Add link to a demo

    Hi, I tried using the notebook in the example folder but it wasn't working (I think the files were not imported into Colab), so I created a demo which should work.

    opened by equiet 1
  • Better code for image_classification.py

    Better code for image_classification.py

    Main change: Used a dict instead of excessive elifs. Other smaller changes.

    Important: I do not have the resources to test the code, but technically, it's just a rewrite of the original, so it should work.

    opened by pinjuf 1
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires numpy, which is not installed.
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires numpy, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade ubuntu from 21.10 to jammy

    [Snyk] Security upgrade ubuntu from 21.10 to jammy

    This PR was automatically created by Snyk using the credentials of a real user.


    Keeping your Docker base image up-to-date means you’ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:jammy, as this image has only 10 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Priority Score / 1000 | Issue | Exploit Maturity | | :------: | :-------------------- | :---- | :--------------- | | medium severity | 514 | Out-of-bounds Read
    SNYK-UBUNTU2110-E2FSPROGS-2770726 | No Known Exploit | | medium severity | 300 | NULL Pointer Dereference
    SNYK-UBUNTU2110-KRB5-1735754 | No Known Exploit | | medium severity | 300 | OS Command Injection
    SNYK-UBUNTU2110-OPENSSL-2933132 | No Known Exploit | | medium severity | 300 | Inadequate Encryption Strength
    SNYK-UBUNTU2110-OPENSSL-2941384 | No Known Exploit | | medium severity | 300 | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU2110-PERL-1930909 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by geekjr 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 571/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.7 | Denial of Service (DoS)
    SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
    3.20.1 -> 3.20.2
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade ubuntu from rolling to 21.10

    [Snyk] Security upgrade ubuntu from rolling to 21.10

    Keeping your Docker base image up-to-date means you’ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:21.10, as this image has only 12 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Issue | Exploit Maturity | | :------: | :---- | :--------------- | | medium severity | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU2110-PERL-1930909 | No Known Exploit | | low severity | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU2110-SHADOW-1758374 | No Known Exploit | | low severity | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU2110-SHADOW-1758374 | No Known Exploit | | low severity | NULL Pointer Dereference
    SNYK-UBUNTU2110-TAR-1744334 | No Known Exploit | | medium severity | CVE-2018-25032
    SNYK-UBUNTU2110-ZLIB-2433596 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade ubuntu from 18.04 to rolling

    [Snyk] Security upgrade ubuntu from 18.04 to rolling

    Keeping your Docker base image up-to-date means you’ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:rolling, as this image has only 13 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Priority Score / 1000 | Issue | Exploit Maturity | | :------: | :-------------------- | :---- | :--------------- | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU1804-PERL-1930908 | No Known Exploit | | low severity | 150 | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU1804-SHADOW-306209 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0

    [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 471/1000
    Why? Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.19.5 -> 1.22.0
    | No | No Known Exploit low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321969 | numpy:
    1.19.5 -> 1.22.0
    | No | Proof of Concept low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.19.5 -> 1.22.0
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0rc1

    [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0rc1

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321969 | numpy:
    1.19.5 -> 1.22.0rc1
    | No | Proof of Concept low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.19.5 -> 1.22.0rc1
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
  • [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.1 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-SETUPTOOLS-3180412 | setuptools:
    39.0.1 -> 65.5.1
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires numpy, which is not installed.
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 441/1000
    Why? Recently disclosed, Has a fix available, CVSS 3.1 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-SETUPTOOLS-3113904 | setuptools:
    39.0.1 -> 65.5.1
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 571/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.7 | Denial of Service (DoS)
    SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
    3.20.1 -> 3.20.2
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    πŸ›  Adjust project settings

    πŸ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    πŸ¦‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
Releases(2.0.0)
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning

Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning This is the official repository of "Camera Distortion-

Hanbyel Cho 12 Oct 06, 2022
This is a model made out of Neural Network specifically a Convolutional Neural Network model

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternativ

9 Oct 18, 2022
πŸ€– Project template for your next awesome AI project. 🦾

πŸ€– AI Awesome Project Template πŸ‘‹ Template author You may want to adjust badge links in a README.md file. πŸ’Ž Installation with pip Installation is as

Wiktor Łazarski 18 Nov 23, 2022
Fast, accurate and reliable software for algebraic CT reconstruction

KCT CBCT Fast, accurate and reliable software for algebraic CT reconstruction. This set of software tools includes OpenCL implementation of modern CT

VojtΔ›ch Kulvait 4 Dec 14, 2022
Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

Deep Learning for image classification pip install -r http://webia.lip6.fr/~baskiotisn/requirements-amal.txt Train an autoencoder python3 train_auto

Hector Kohler 0 Mar 30, 2022
The codes and models in 'Gaze Estimation using Transformer'.

GazeTR We provide the code of GazeTR-Hybrid in "Gaze Estimation using Transformer". We recommend you to use data processing codes provided in GazeHub.

65 Dec 27, 2022
Reproduction of Vision Transformer in Tensorflow2. Train from scratch and Finetune.

Vision Transformer(ViT) in Tensorflow2 Tensorflow2 implementation of the Vision Transformer(ViT). This repository is for An image is worth 16x16 words

sungjun lee 42 Dec 27, 2022
Simulator for FRC 2022 challenge: Rapid React

rrsim 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] wh

1 Jan 18, 2022
This script scrapes and stores the availability of timeslots for Car Driving Test at all RTA Serivce NSW centres in the state.

This script scrapes and stores the availability of timeslots for Car Driving Test at all RTA Serivce NSW centres in the state. Dependencies Account wi

Balamurugan Soundararaj 21 Dec 14, 2022
Audio Visual Emotion Recognition using TDA

Audio Visual Emotion Recognition using TDA RAVDESS database with two datasets analyzed: Video and Audio dataset: Audio-Dataset: https://www.kaggle.com

Combinatorial Image Analysis research group 3 May 11, 2022
Azion the best solution of Edge Computing in the world.

Azion Edge Function docker action Create or update an Edge Functions on Azion Edge Nodes. The domain name is the key for decision to a create or updat

8 Jul 16, 2022
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma This repo provi

Jingtao Zhan 99 Dec 27, 2022
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022
Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN Official PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. Prerequisites Python 2.7

SK T-Brain 754 Dec 29, 2022
Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

Grow Function: A 3D Stacked Bifurcating Double Deep Cellular Automata which differentiates using a Genetic Algorithm... TLDR;High Def Trees that you can mint as NFTs on Solana

Nathaniel Gibson 4 Oct 08, 2022
Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network

Introduction This repository is about paper SpeakerGAN , and is unofficially implemented by Mingming Huang ( 7 Jan 03, 2023

codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress

Jamie Simon 45 Dec 02, 2022
Simultaneous Detection and Segmentation

Simultaneous Detection and Segmentation This is code for the ECCV Paper: Simultaneous Detection and Segmentation Bharath Hariharan, Pablo Arbelaez,

Bharath Hariharan 96 Jul 20, 2022
[AAAI-2022] Official implementations of MCL: Mutual Contrastive Learning for Visual Representation Learning

Mutual Contrastive Learning for Visual Representation Learning This project provides source code for our Mutual Contrastive Learning for Visual Repres

winycg 48 Jan 02, 2023
The offcial repository for 'CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos', SIGIR2022

CharacterBERT-DR The offcial repository for CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos, Sh

ielab 11 Nov 15, 2022