#计算机科学#Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, N...
DeepLab v3+ model in PyTorch. Support different backbones.
Classification models trained on ImageNet. Keras.
#计算机科学#Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework
Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, Enc...
猫狗大战
#计算机科学#COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were tra...
#计算机科学#A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, ...
This is an ongoing re-implementation of DeepLab_v3_plus on pytorch which is trained on VOC2012 and use ResNet101 for backbone.
#计算机科学#Deep learning based tool for image processing. No need for Programing and GPU.
Lightweight Facial Expression(emotion) Recognition model
#计算机科学#Easy-to-use scripts for training and inferencing with Xception on your own dataset
Train/Eval the popular network by TF-Slim,include mobilenet/shufflenet/squeezenet/resnet/inception/vgg/alexnet
#计算机科学#Learning a Deep Dual-level Network for Robust DeepFake Detection
#计算机科学#This GitHub repository contains instructions for downloading and utilizing the AI4Food-NutritionDB food image database, as well as different food recognition systems based on Xception and EfficientNet...
#计算机科学#AI-generated or real face? These Deep Learning-based models can expose digital imposters before they ghost you, so no more falling for flawless deepfake faces!!
#计算机科学#Benchmarking various Computer Vision models on TinyImageNet Dataset