Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

DeepLab v3+ model in PyTorch. Support different backbones.

Classification models trained on ImageNet. Keras.

Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)

This is an ongoing re-implementation of DeepLab_v3_plus on pytorch which is trained on VOC2012 and use ResNet101 for backbone.

Lightweight Facial Expression(emotion) Recognition model

Deep learning based tool for image processing. No need for Programing and GPU.

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

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 EfficientNetV2 architectures.

Learning a Deep Dual-level Network for Robust DeepFake Detection

Benchmarking various Computer Vision models on TinyImageNet Dataset

Simple Eye Blink Detection with CNN

Here is an implementation of DeepLabv3+ in PyTorch(1.7). It supports many backbones and datasets.

This GitHub provides different DeepFakes Detectors using facial regions and considering three different state-of-the-art fake detection systems.

VerifyVision-Pro是一个全面的图像伪造篡改检测解决方案，利用深度学习（deep learning）和计算机视觉技术（cv）精确识别各类图像篡改，包括deepfake、AI生成内容、拼接操作和复制-移动篡改。基于PyTorch实现，集成了从数据处理、模型训练到部署的完整工作流程。

Pytorch implementation of DeepLab V3+

Xception V1 model in Tensorflow with pretrained weights on ImageNet

Tagging images of bank cheques

pytorch-1.1 torchvision-0.3