O objetivo da rede é conseguir distinguir 5 tipos de carros diferentes, sendo eles: Bugatti, Ferrari, Fusca, Lamborghini e Porsche. A Rede foi desenvolvida no Google Colab.
Semi-supervised Domain Adaptive Structure Learning - ASDA This repo contains the source code and dataset for our ASDA paper. Illustration of the propo
Autonomous Movement from Simultaneous Localization and Mapping About us Built by a group of Clarkson University students with the help from Professor
How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model
TextureGAN This code is our PyTorch implementation of TextureGAN [Project] [Arxiv] TextureGAN is a generative adversarial network conditioned on sketc
xview3-kohei-solution Usage This repository assumes that the given data set is stored in the following locations: $ ls data/input/xview3/*.csv data/in
imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection.
CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.
Contextual Attention Network: Transformer Meets U-Net Contexual attention network for medical image segmentation with state of the art results on skin
DSAMNet The pytorch implementation for "A Deeply-supervised Attention Metric-based Network and an Open Aerial Image Dataset for Remote Sensing Change
SberSwap Video Swap base on deep learning
fastshap: A fast, approximate shap kernel fastshap was designed to be: Fast Calculating shap values can take an extremely long time. fastshap utilizes
Who's there? The spiritual successor to knockknock for PyTorch Lightning, to get a notification when your training is complete or when it crashes duri
Auto-Lama Auto-Lama combines object detection and image inpainting to automate object removals. It is build on top of DE:TR from Facebook Research and
FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation(ICPR 2020) Overview This code is for the paper: Spatial Attention U-Net for Retinal V
Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. 🔥
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat
太极图形课S1-标题部分 这个作业未来或将是你的开源项目，标题的内容可以来自作业中的核心关键词，让读者一眼看出你所完成的工作/做出的好玩demo 如果暂时未想好，起名时可以参考“太极图形课S1-xxx作业” 如下是作业（项目）展开说明的方法，可以帮大家理清思路，并且也对读者非常友好，请小伙伴们多多参
implementation of MM1 and MMC Queue on randomly generated data and evaluate simulation results then compare with analytical results and draw a plot curve for them, simulate some integrals and compare
PyTorch implementation of DUL (Data Uncertainty Learning in Face Recognition, CVPR2020)
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