A stand-alone version of the pretrained MxNet Gluon ResNet models ported to Pytorch. Currently part of my model collection (https://github.com/rwightman/pytorch-image-models/blob/master/models/gluon_resnet.py)
This model covers all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet found in the gluon model zoo (https://gluon-cv.mxnet.io/model_zoo/classification.html, https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/model_zoo.py) that
- have stride in 3x3 conv layer of bottleneck
- have conv-bn-act ordering
Included ResNet variants are:
- v1b - 7x7 stem, stem_width=64, same as torchvision ResNet (checkpoint compatible), or NVIDIA ResNet 'v1.5'
- v1c - 3 layer deep 3x3 stem, stem_width = 32
- v1d - 3 layer deep 3x3 stem, stem_width = 32, average pool in downsample
- v1e - 3 layer deep 3x3 stem, stem_width = 64, average pool in downsample *no pretrained weights available
- v1s - 3 layer deep 3x3 stem, stem_width = 64
ResNeXt is standard and checkpoint compatible with torchvision pretrained models. 7x7 stem, stem_width = 64, standard cardinality and base width calcs
SE-ResNeXt is standard. 7x7 stem, stem_width = 64, checkpoints are not compatible with Cadene pretrained, but could be with key mapping
SENet-154 is standard. 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64, reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
Original ResNet-V1, ResNet-V2 (bn-act-conv), and SE-ResNet (stride in first bottleneck conv) are NOT supported.
| model | top1 (err) | top5 (err) | param_count |
| gluon_resnet18_v1b | 70.830 (29.170) | 89.756 (10.244) | 11.69 |
| gluon_resnet34_v1b | 74.580 (25.420) | 91.988 (8.012) | 21.8 |
| gluon_resnet50_v1b | 77.578 (22.422) | 93.718 (6.282) | 25.56 |
| gluon_resnet50_v1c | 78.010 (21.990) | 93.988 (6.012) | 25.58 |
| gluon_resnet50_v1s | 78.712 (21.288) | 94.242 (5.758) | 25.68 |
| gluon_resnet50_v1d | 79.074 (20.926) | 94.476 (5.524) | 25.58 |
| gluon_resnet101_v1b | 79.304 (20.696) | 94.524 (5.476) | 44.55 |
| gluon_resnext50_32x4d | 79.356 (20.644) | 94.424 (5.576) | 25.03 |
| gluon_resnet101_v1c | 79.544 (20.456) | 94.586 (5.414) | 44.57 |
| gluon_resnet152_v1b | 79.692 (20.308) | 94.738 (5.262) | 60.19 |
| gluon_seresnext50_32x4d | 79.912 (20.088) | 94.818 (5.182) | 27.56 |
| gluon_resnet152_v1c | 79.916 (20.084) | 94.842 (5.158) | 60.21 |
| gluon_resnet101_v1s | 80.300 (19.700) | 95.150 (4.850) | 44.67 |
| gluon_resnext101_32x4d | 80.334 (19.666) | 94.926 (5.074) | 44.18 |
| gluon_resnet101_v1d | 80.424 (19.576) | 95.020 (4.980) | 44.57 |
| gluon_resnet152_v1d | 80.470 (19.530) | 95.206 (4.794) | 60.21 |
| gluon_resnext101_64x4d | 80.602 (19.398) | 94.994 (5.006) | 83.46 |
| gluon_seresnext101_64x4d | 80.890 (19.110) | 95.304 (4.696) | 88.23 |
| gluon_seresnext101_32x4d | 80.902 (19.098) | 95.294 (4.706) | 48.96 |
| gluon_resnet152_v1s | 81.012 (18.988) | 95.416 (4.584) | 60.32 |
| gluon_senet154 | 81.224 (18.776) | 95.356 (4.644) | 115.09 |
Models can be access via the PyTorch Hub API
>>> torch.hub.list('rwightman/pytorch-pretrained-gluonresnet')
['gluon_resnet18_v1b', ...]
>>> model = torch.hub.load('rwightman/pytorch-pretrained-gluonresnet', 'gluon_resnet50_v1d', pretrained=True)
>>> model.eval()
>>> output = model(torch.randn(1,3,224,224))