Skip to content
#

pytorch-cnn-classification

Here are 14 public repositories matching this topic...

Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.

  • Updated Jul 6, 2018
  • Jupyter Notebook

DogBreedSpotter is a Python-based image classification project designed to identify and classify dog breeds in images. This project utilizes deep learning models, including convolutional neural networks (CNNs) such as VGG, AlexNet, and ResNet, to accurately detect whether an image contains a dog and, if so, determine the breed.

  • Updated Sep 9, 2023
  • CSS

Photo sharing and photo storage services like to have location data for each photo that is uploaded. With the location data, these services can build advanced features, such as automatic suggestion of relevant tags or automatic photo organization, which help provide a compelling user experience.

  • Updated Oct 14, 2023
  • HTML

Improve this page

Add a description, image, and links to the pytorch-cnn-classification topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the pytorch-cnn-classification topic, visit your repo's landing page and select "manage topics."

Learn more