cifar10-fast

ResNet demo

Demonstrates training a small ResNet on CIFAR10 with a specific configuration and benchmarking setup

GitHub

532 stars
15 watching
119 forks
Language: Jupyter Notebook
last commit: almost 3 years ago

Related projects:

Repository Description Stars
zygmuntz/kaggle-cifar Code for training and predicting on the CIFAR-10 image classification dataset using CUDA-convnet architecture. 44
nagadomi/kaggle-cifar10-torch7 A deep neural network architecture optimized for image classification on the CIFAR-10 dataset 246
guillaume-chevalier/hyperopt-keras-cnn-cifar-100 Automates hyperparameter optimization and neural network architecture search using Hyperopt on a CNN model for the CIFAR-100 dataset 106
rsvp/fecon235 Provides an interface to financial economics data and analysis tools 1,145
rlan/notebooks A runtime environment for machine learning via Jupyter notebooks. 32
prlz77/resnext.pytorch Reproduces ResNet-V3 with PyTorch for computer vision tasks 508
hpcgarage/accelerated_dl_pytorch This repository provides tutorials and demo code for accelerated deep learning with PyTorch using Jupyter Notebook. 127
locuslab/convmixer-cifar10 A simple ConvMixer-based classification system for the CIFAR-10 dataset 41
jakevdp/sklearn_pycon2015 A collection of materials for teaching scikit-learn to Python developers 895
britefury/deep-learning-tutorial-pydata A tutorial project providing guidance on building and training deep learning models using PyData 85
yuyang-huang/keras-inception-resnet-v2 Represents an implementation of the Inception-ResNet v2 deep learning model in Keras. 180
eniac-xie/faster-rcnn-resnet An implementation of Faster R-CNN using ResNet architecture with online hard example mining for object detection 207
soumith/cvpr2015 An introduction to deep learning for computer vision using the Torch framework 868
bigballon/cifar-zoo Provides implementations of CNN architectures and improvement methods for image classification on the CIFAR benchmark. 700
opensourcesec/cirtkit A comprehensive toolset for digital forensics and incident response analysis using Python 142