|
setting |
#P |
GFLOPs |
PyTorch |
Gluon |
ResNeSt-50-fast |
1s1x64d |
26.3M |
4.34 |
80.33 |
80.35 |
ResNeSt-50-fast |
2s1x64d |
27.5M |
4.34 |
80.53 |
80.65 |
ResNeSt-50-fast |
4s1x64d |
31.9M |
4.35 |
80.76 |
80.90 |
ResNeSt-50-fast |
1s2x40d |
25.9M |
4.38 |
80.59 |
80.72 |
ResNeSt-50-fast |
2s2x40d |
26.9M |
4.38 |
80.61 |
80.84 |
ResNeSt-50-fast |
4s2x40d |
30.4M |
4.41 |
81.14 |
81.17 |
ResNeSt-50-fast |
1s4x24d |
25.7M |
4.42 |
80.99 |
80.97 |
import torch
# get list of models
torch.hub.list('zhanghang1989/ResNeSt', force_reload=True)
# load pretrained models, using ResNeSt-50-fast_2s1x64d as an example
net = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50_fast_2s1x64d', pretrained=True)
- Load using python package
# using ResNeSt-50 as an example
from resnest.torch import resnest50_fast_2s1x64d
net = resnest50_fast_2s1x64d(pretrained=True)
# using ResNeSt-50 as an example
from resnest.gluon import resnest50_fast_2s1x64d
net = resnest50_fast_2s1x64d(pretrained=True)