Training Cifar-10 Classifier Using VGG16

Last update: Aug 17, 2022

Related tags

Deep Learning cvdl-hw3

Overview

opevcvdl-hw3

This project uses pytorch and Qt to achieve the requirements.

Version

Python 3.6
opencv-contrib-python 3.4.2.17
Matplotlib 3.1.1
pyqt5 5.15.1
Tensorflow 2.0.0
PyTorch 1.3.0

Requirements

Training Cifar-10 Classifier Using VGG16

Owner

Kenny Cheng

Master Student at NCKU Autonomous Vehicles Team

GitHub Repository

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Training Cifar-10 Classifier Using VGG16

Related tags

Overview

opevcvdl-hw3

Version

Requirements

Owner

Kenny Cheng

Code for the paper: Fighting Fake News: Image Splice Detection via Learned Self-Consistency

PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

This is a deep learning-based method to segment deep brain structures and a brain mask from T1 weighted MRI.

A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery

Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

PyTorch reimplementation of the paper Involution: Inverting the Inherence of Convolution for Visual Recognition [CVPR 2021].

Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Scalable implementation of Lee / Mykland (2012) and Ait-Sahalia / Jacod (2012) Jump tests for noisy high frequency data

Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".

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Classifies galaxy morphology with Bayesian CNN

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Code for "Discovering Non-monotonic Autoregressive Orderings with Variational Inference" (paper and code updated from ICLR 2021)

Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"

A curated (most recent) list of resources for Learning with Noisy Labels

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