There are 0 repository under resnets topic.
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
Code for DCASE 2020 task 1a and task 1b.
practice on CIFAR10 with PyTorch
Replace FC2, LeNet-5, VGG, Resnet, Densenet's full-connected layers with MPO
Implementation of "Weight Averaging Improves Knowledge Distillation under Domain Shift" (ICCV 2023 OOD-CV Workshop)
Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)
Single-sequence and Profile-based Prediction of RNA Solvent Accessibility Using Dilated Convolution Neural Network
train resnet(152/101/50 layers) for iNaturalist Challenge at FGVC 2018 with tensorpack
This is an AI-powered advertisement platform that performs Face Detection using Haar Cascade Frontal Face and Wide ResNet to detect your age and gender via an external camera and then show you video advertisements related to the products that suit your corresponding age and gender in real-time.
The experiment of project
:footprints: “恒锐杯”鞋印花纹图像类别判定挑战赛
A computer vision web-app that uses deep learning and Residual Neural Networks to identify your Pokémon and then tells you all about it.
Robustness of Deep Neural Networks using Trainable Activation Functions
The aim is to build a Deep Convolutional Network using Residual Networks (ResNet). Here we build ResNet 50 using Keras.
Yolov3 with multi backbone
Simple Multi-GPU Implementation of ResNet in Tensorflow
High Accuracy ResNet Model under 5 Million parameters.
Pytorch Implementation and Performance Analysis of the Popular Vision Architectures from Scratch.
Optimize ResNet Learning Process
Training using an alternative approach: forward-thinking
Simple implementation of a residually connected convolutional neural network in PyTorch
An experiment in training a fully connected residual net to learn the argmax function.
A collection of small-scale projects that helped me learn the basics of the PyTorch framework
A set of experiments inspired by the paper "Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs" by Jonathan Frankle, David J. Schwab, Ari S. Morcos
This Project uses Convolutional Neural Networks (CNN) for the classification and prediction of handwritten Devanagari script. Leveraging transfer learning techniques, it adapts pre-trained models to recognize and forecast characters in Devanagari, enhancing accuracy and efficiency.
Journey to Learn Deep Learning with Pytorch from scratch i.e, from Tensor & Gradients to Advance topic like Generative Adversarial Networks