ModelFeast

中文版readme

Please star ModelFeast if it helps you. This is very important to me! Thanks very much !

ModelFeast is more than model-zoo! It is:

• A gather of the most popular 2D, 3D CNN models
• A tool to make deep learn much more simply and flexibly
• A pytorch project template

What is ModelFeast ?

Avalible models

2D CNN

• Xception
• InceptionV3
• InceptionResnetV2
• SqueezeNet1_0, SqueezeNet1_1
• VGG11, VGG13, VGG16, VGG19
• ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
• ResNext101_32x4d, ResNext101_64x4d
• DenseNet121, DenseNet169, DenseNet201, DenseNet161

Pretrained models ( trained on ImageNet ) for 2D CNN is now avalible on Baiduyun(fst6) and Google Drive

3D CNN

• resnet18v2_3d, resnet34v2_3d, resnet50v2_3d, resnet101v2_3d, resnet152v2_3d, resnet200v2_3d
• resnext50_3d, resnext101_3d, resnext152_3d
• densenet121_3d, densenet169_3d, densenet201_3d, densenet264_3d
• resnet10_3d, resnet18_3d, resnet34_3d, resnet101_3d, resnet152_3d, resnet200_3d
• wideresnet50_3d
• i3d50, i3d101, i3d152

CNN-RNN

This part is still on progress. Not avalible to train now, but model architecture can been seen here.

Get started

Determine what you need and read corresponding tutorials

• I want to train a model as simple as possible
• I just need the codes of CNNs
• I need a standard pytorch project template

Or you can use modelfeast simply via pip !

pip3 install modelfeast

pip user guide

Features

The features are more than you could think of:

• Train and save model within 3 lines !
• Multi GPU support !
• Include the most popular 2D CNN, 3D CNN, and CRNN models !
• Allow any input image size (pytorch official model zoo limit your input size harshly) !
• Help you sweep all kinds of classification competitions.

Reference

https://github.com/lanpa/tensorboardX

https://github.com/pytorch/vision/tree/master/torchvision/models

https://github.com/kenshohara/3D-ResNets-PyTorch

https://github.com/victoresque/pytorch-template

https://github.com/AlexHex7/Non-local_pytorch

https://github.com/Cadene/pretrained-models.pytorch