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

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings

    📚 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

    🛠 Adjust project settings

    📚 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

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 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

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
Releases(2.0.0)
Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS an

Chair for Sys­tems Se­cu­ri­ty 541 Nov 27, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

SSAN Introduction This is the pytorch implementation of the SSAN model (see our AAAI2021 paper: Entity Structure Within and Throughout: Modeling Menti

benfeng 69 Nov 15, 2022
The pure and clear PyTorch Distributed Training Framework.

The pure and clear PyTorch Distributed Training Framework. Introduction Requirements and Usage Dependency Dataset Basic Usage Slurm Cluster Usage Base

WILL LEE 208 Dec 20, 2022
A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis

A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis This is the pytorch implementation for our MICCAI 2021 paper. A Mul

Jiarong Ye 7 Apr 04, 2022
Multimodal Co-Attention Transformer (MCAT) for Survival Prediction in Gigapixel Whole Slide Images

Multimodal Co-Attention Transformer (MCAT) for Survival Prediction in Gigapixel Whole Slide Images [ICCV 2021] © Mahmood Lab - This code is made avail

Mahmood Lab @ Harvard/BWH 63 Dec 01, 2022
Consecutive-Subsequence - Simple software to calculate susequence with highest sum

Simple software to calculate susequence with highest sum This repository contain

Gbadamosi Farouk 1 Jan 31, 2022
Jittor Medical Segmentation Lib -- The assignment of Pattern Recognition course (2021 Spring) in Tsinghua University

THU模式识别2021春 -- Jittor 医学图像分割 模型列表 本仓库收录了课程作业中同学们采用jittor框架实现的如下模型: UNet SegNet DeepLab V2 DANet EANet HarDNet及其改动HarDNet_alter PSPNet OCNet OCRNet DL

48 Dec 26, 2022
Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

Phil Wang 65 Oct 04, 2022
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Preprocess file of the dataset used in implicit sub-populations: (Demographic groups

<a href=[email protected]"> 4 Oct 14, 2022
constructing maps of intellectual influence from publication data

Influencemap Project @ ANU Influence in the academic communities has been an area of interest for researchers. This can be seen in the popularity of a

CS Metrics 13 Jun 18, 2022
PyTorch implementation for paper "Full-Body Visual Self-Modeling of Robot Morphologies".

Full-Body Visual Self-Modeling of Robot Morphologies Boyuan Chen, Robert Kwiatkowskig, Carl Vondrick, Hod Lipson Columbia University Project Website |

Boyuan Chen 32 Jan 02, 2023
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering This repository provides the source code of "Consensus Learning

SeongKu-Kang 6 Apr 29, 2022
Face Recognition plus identification simply and fast | Python

PyFaceDetection Face Recognition plus identification simply and fast Ubuntu Setup sudo pip3 install numpy sudo pip3 install cmake sudo pip3 install dl

Peyman Majidi Moein 16 Sep 22, 2022
A two-stage U-Net for high-fidelity denoising of historical recordings

A two-stage U-Net for high-fidelity denoising of historical recordings Official repository of the paper (not submitted yet): E. Moliner and V. Välimäk

Eloi Moliner Juanpere 57 Jan 05, 2023
This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".

Ad^2Attack:Adaptive Adversarial Attack on Real-Time UAV Tracking Demo video 📹 Our video on bilibili demonstrates the test results of Ad^2Attack on se

Intelligent Vision for Robotics in Complex Environment 10 Nov 07, 2022
Gender Classification Machine Learning Model using Sk-learn in Python with 97%+ accuracy and deployment

Gender-classification This is a ML model to classify Male and Females using some physical characterstics Data. Python Libraries like Pandas,Numpy and

Aryan raj 11 Oct 16, 2022
Code and Resources for the Transformer Encoder Reasoning Network (TERN)

Transformer Encoder Reasoning Network Code for the cross-modal visual-linguistic retrieval method from "Transformer Reasoning Network for Image-Text M

Nicola Messina 53 Dec 30, 2022
A web application that provides real time temperature and humidity readings of a house.

About A web application which provides real time temperature and humidity readings of a house. If you're interested in the data collected so far click

Ben Thompson 3 Jan 28, 2022
Customizable RecSys Simulator for OpenAI Gym

gym-recsys: Customizable RecSys Simulator for OpenAI Gym Installation | How to use | Examples | Citation This package describes an OpenAI Gym interfac

Xingdong Zuo 14 Dec 08, 2022