Implemented digit detector in natural scene using resnet50 and Yolo-v2. I used SVHN as the training set, and implemented it using tensorflow and keras.
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Updated
Mar 24, 2023 - Jupyter Notebook
Implemented digit detector in natural scene using resnet50 and Yolo-v2. I used SVHN as the training set, and implemented it using tensorflow and keras.
Official adversarial mixup resynthesis repository
A pip-installable evaluator for GANs (IS and FID). Accepts either dataloaders or individual batches. Supports on-the-fly evaluation during training. A working DCGAN SVHN demo script provided.
This project is a PyTorch implementation that uses deep CNN to recognize multi-digit numbers using the SVHN dataset derived from Google Street View house numbers, each picture contains a set of numbers from 0 to 9, the model is tested to have 89% accuracy.| 使用深度卷积神经网络从街景图像中识别多位数门牌号的PyTorch实现方案,使用的数据集为SVHN,来源于谷歌街景门牌号码,每张图片中包含一组0-9的阿拉伯数字,经测试精确度可达89%
Pytorch implementation of homework 2 for VRDL course in 2021 Fall semester at NYCU.
Different convolutional neural network implementations for predicting the lenght of the house numbers in the SVHN image dataset. First part of the Humanware project in ift6759-avanced projects in ML.
Homeworks from CS294-158-19 (Deep unsupervised learning) implemented in Pytorch
An Exploration of Machine Learning Methods on SVHN Dataset
An implementation of a Convolutional VAE on the SVHN dataset.
Second Assignment in 'Practical topics in Machine Learning' course by Dr. Kfir Bar at Bar-Ilan University
Using attention for sequence classification for multi-character prediction
A project constructing an image representation model via unsupervised and self-supervised learning.
Convolutional Neural Network created using Keras with tensorflow backend. Machine learning model used to predict the digit on a photo.
Numerical Digit Detection and Classification on SNVH Dataset
Solutions for Advanced Image Analysis course assignments, featuring model designs for image summation and generation with MNIST, and style transfer using CycleGAN with MNIST and SVHN datasets.
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