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ai_challenger_2018

AI Challenger, a platform for open datasets and programming competitions to artificial intelligence (AI) talents around the world. https://challenger.ai/

alae

[CVPR2020] Adversarial Latent Autoencoders

bbn

The official PyTorch implementation of paper BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition

competition-baseline

数据科学竞赛各种baseline代码、思路分享

course-v3

The 3rd edition of course.fast.ai

deepfacelab

DeepFaceLab is the leading software for creating deepfakes.

faceswap

Deepfakes Software For All

grad-cam.pytorch

pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;欢迎试用、关注并反馈问题...

humpback-whale-identification-1st-

https://www.kaggle.com/c/humpback-whale-identification

hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

image-similarity-deep-ranking

imagededup

😎 Finding duplicate images made easy!

ipywidgets

Interactive Widgets for the Jupyter Notebook

kaggle-ensemble-guide

Code for the Kaggle Ensembling Guide Article on MLWave

kaggle_pbr

My best submission to the Kaggle competition "Predicting a Biological Response", ranked 17th over 711 teams.

kaggle_titanic

the data and ipython notebook of my attempt to solve the kaggle titanic problem

keras

Keras' fork with several new functionalities. Caffe2Keras converter, multimodal layers, etc.

keras_learning

mosquitodl

plantvillage_deeplearning_paper_dataset

plantvillage_deeplearning_step_by_step

This project is trying to record every detail in the process of building and deploying a successful deep learning model.

python-3-object-oriented-programming-third-edition

Python 3 Object-Oriented Programming – Third Edition, published by Packt

pytorch-tutorial

Build your neural network easy and fast

pytorch_yolov3

Implementation of YOLOv3 in PyTorch

sfc2net

Scale-Fusion Classification Counting Network

stylized-imagenet

Code to create Stylized-ImageNet, a stylized version of standard ImageNet (ICLR 2019 Oral)

texture-vs-shape

Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral)