resnet-34

Detect and recognize the faces from camera / 调用摄像头进行人脸识别，支持多张人脸同时识别

毕业设计 使用pytorch进行深度学习网络模型训练，实现车型识别

UNet architecture and Keras code with ResBlock for segmentation

Malaria Detection using Deep Learning

This repository presents an innovative approach to classifying blood groups using fingerprint images through deep learning techniques. The project explores state-of-the-art convolutional neural network (CNN) architectures, such as ResNet, VGG16, AlexNet, and LeNet, to analyze and predict blood groups accurately.

ResNet-34 implementation of the paper "Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles" in Keras

Research model for classification and feature extraction of dermatoscopic images

My PyTorch implementation of CNNs. All networks in this repository are using CIFAR-100 dataset for training.

AnimalsClassificationModel is a Python app that classifies animals from images using a ResNet34 model. It features a Streamlit interface and utilizes Plotly for visualization.

Deep learning experiments to design a model to predict Parkinson's diseases with the images of Spiral/Wave test

Identification of road surfaces and 12 different classes like speed bumps, paved, unpaved, markings, water puddles, potholes, etc.

Various Classical Deep-learning Algorithm coded by Tensorflow and Pytorch framework

Deep learning based solution to automatically analyze medical images for malaria testing

[Open Source]. ARGAN - The improved version of AnimeGAN. Landscape photos/videos to anime

ResNet-34 Model trained from scratch to classify 450 different species of birds with 98.6% accuracy.

Multi-label classification of defective solar cells with PyTorch using a pre-trained residual neural network

Code showing how to port ResNet Pytorch weights to Tensorflow 2.0

毕业设计 基于深度学习的车型识别APP，此处为APP模块

Segmentation model using UNET architecture with ResNet34 as encoder background, designed with PyTorch.

Implementation for Video Human Activity Recognition using OpenCV