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:
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
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