from fastai.gen_doc.nbdoc import *
from fastai.vision.models.darknet import Darknet
from fastai.vision.models.wrn import wrn_22, WideResNet
The fastai library includes several pretrained models from torchvision, namely:
On top of the models offered by torchvision, fastai has implementations for the following models:
models.unetshow_doc(Darknet)
class Darknet[source][test]
Darknet(num_blocks:Collection[int],num_classes:int,nf=*32*) ::PrePostInitMeta::Module
No tests found for Darknet. To contribute a test please refer to this guide and this discussion.
Create a Darknet with blocks of sizes given in num_blocks, ending with num_classes and using nf initial features. Darknet53 uses num_blocks = [1,2,8,8,4].
show_doc(WideResNet)
class WideResNet[source][test]
WideResNet(num_groups:int,N:int,num_classes:int,k:int=*1,drop_p:float=0.0,start_nf:int=16,n_in_channels:int=3*) ::PrePostInitMeta::Module
No tests found for WideResNet. To contribute a test please refer to this guide and this discussion.
Wide ResNet with num_groups and a width of k.
Each group contains N blocks. start_nf the initial number of features. Dropout of drop_p is applied in between the two convolutions in each block. The expected input channel size is fixed at 3.
Structure: initial convolution -> num_groups x N blocks -> final layers of regularization and pooling
The first block of each group joins a path containing 2 convolutions with filter size 3x3 (and various regularizations) with another path containing a single convolution with a filter size of 1x1. All other blocks in each group follow the more traditional res_block style, i.e., the input of the path with two convs is added to the output of that path.
In the first group the stride is 1 for all convolutions. In all subsequent groups the stride in the first convolution of the first block is 2 and then all following convolutions have a stride of 1. Padding is always 1.
show_doc(wrn_22)
wrn_22[source][test]
wrn_22()
No tests found for wrn_22. To contribute a test please refer to this guide and this discussion.
Wide ResNet with 22 layers.
This is a WideResNet with num_groups=3, N=3, k=6 and drop_p=0..