Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Classification models trained on ImageNet. Keras.

An Implementation of Fully Convolutional Networks in Tensorflow.

High level network definitions with pre-trained weights in TensorFlow

Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

This is a code repository for pytorch c++ (or libtorch) tutorial.

Computer vision based ML training data generation tool 🚀

Repository for Single Shot MultiBox Detector and its variants, implemented with pytorch, python3.

food image to recipe with deep convolutional neural networks.

仅使用numpy从头开始实现神经网络,包括反向传播公式推导过程; numpy构建全连接层、卷积层、池化层、Flatten层；以及图像分类案例及精调网络案例等,持续更新中... ...

天池医疗AI大赛[第一季]：肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet

This implements training of popular model architectures, such as AlexNet, ResNet and VGG on the ImageNet dataset(Now we supported alexnet, vgg, resnet, squeezenet, densenet)

ImageNet pre-trained models with batch normalization for the Caffe framework

Implement of Openpose use Tensorflow

Artificial Intelligence Learning Notes.

AI Papers

Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.

RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

Learning and Building Convolutional Neural Networks using PyTorch