Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet101_v1b |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet101_v1c |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet101_v1d |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet101_v1s |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet152_v1b |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet152_v1c |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet152_v1d |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet152_v1s |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet18_v1b |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet34_v1b |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet50_v1b |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet50_v1c |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet50_v1d |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
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ID | gluon_resnet50_v1s |
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Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack residual blocks ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.
The weights from this model were ported from Gluon.
To load a pretrained model:
import timm
m = timm.create_model('gluon_resnet18_v1b', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. gluon_resnet18_v1b
. You can find the IDs in the model summaries at the top of this page.
You can follow the timm recipe scripts for training a new model afresh.
@article{DBLP:journals/corr/HeZRS15,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
archivePrefix = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
MODEL | TOP 1 ACCURACY | TOP 5 ACCURACY |
---|---|---|
gluon_resnet152_v1s | 81.02% | 95.42% |
gluon_resnet152_v1d | 80.48% | 95.2% |
gluon_resnet101_v1d | 80.4% | 95.02% |
gluon_resnet101_v1s | 80.29% | 95.16% |
gluon_resnet152_v1c | 79.91% | 94.85% |
gluon_resnet152_v1b | 79.69% | 94.73% |
gluon_resnet101_v1c | 79.53% | 94.59% |
gluon_resnet101_v1b | 79.3% | 94.53% |
gluon_resnet50_v1d | 79.06% | 94.46% |
gluon_resnet50_v1s | 78.7% | 94.25% |
gluon_resnet50_v1c | 78.01% | 93.99% |
gluon_resnet50_v1b | 77.58% | 93.72% |
gluon_resnet34_v1b | 74.59% | 92.0% |
gluon_resnet18_v1b | 70.84% | 89.76% |