| Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
|---|---|
| ID | resnet101d |
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| Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
|---|---|
| ID | resnet152d |
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| Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
|---|---|
| ID | resnet18d |
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| Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
|---|---|
| ID | resnet200d |
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| Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
|---|---|
| ID | resnet26d |
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| Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
|---|---|
| ID | resnet34d |
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| Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
|---|---|
| ID | resnet50d |
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ResNet-D is a modification on the ResNet architecture that utilises an average pooling tweak for downsampling. The motivation is that in the unmodified ResNet, the 1×1 convolution for the downsampling block ignores 3/4 of input feature maps, so this is modified so no information will be ignored
To load a pretrained model:
import timm
m = timm.create_model('resnet50d', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. resnet50d. 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.
@misc{he2018bag,
title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
year={2018},
eprint={1812.01187},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Image Classification on ImageNet
| MODEL | TOP 1 ACCURACY | TOP 5 ACCURACY |
|---|---|---|
| resnet200d | 83.24% | 96.49% |
| resnet152d | 83.13% | 96.35% |
| resnet101d | 82.31% | 96.06% |
| resnet50d | 80.55% | 95.16% |
| resnet34d | 77.11% | 93.38% |
| resnet26d | 76.69% | 93.15% |
| resnet18d | 72.27% | 90.69% |
