Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
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Updated
Jan 13, 2024 - Python
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
PaddlePaddle End-to-End Development Toolkit(飞桨低代码开发工具)
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)
Tensorflow Faster RCNN for Object Detection
OpenMMLab Pre-training Toolbox and Benchmark
Implementation of EfficientNet model. Keras and TensorFlow Keras.
Convolutional Neural Networks to predict the aesthetic and technical quality of images.
Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
Caffe Implementation of Google's MobileNets (v1 and v2)
Library for Fast and Flexible Human Pose Estimation
Classification models trained on ImageNet. Keras.
High level network definitions with pre-trained weights in TensorFlow
PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help
The implementation of various lightweight networks by using PyTorch. such as:MobileNetV2,MobileNeXt,GhostNet,ParNet,MobileViT、AdderNet,ShuffleNetV1-V2,LCNet,ConvNeXt,etc. ⭐⭐⭐⭐⭐
MobileNetV3 in pytorch and ImageNet pretrained models
A mobilenet SSD based face detector, powered by tensorflow object detection api, trained by WIDERFACE dataset.
A face recognition solution on mobile device.
LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)
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)
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