Papers with Code - ResNet (2024)

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{ "Parameters": 62000000 "FLOPs": 524000000 "Training Time": "24 hours", "Training Resources": "8 NVIDIA V100 GPUs", "Training Data": ["ImageNet, Instagram"], "Training Techniques": ["AdamW, CutMix"]}

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Papers with Code - ResNet (1) rwightman / pytorch-image-models

Last updated on Feb 14, 2021

resnet18

Parameters 12 Million

FLOPs 2 Billion

File Size 44.66 MB

Training Data ImageNet

Training Resources

Training Time

Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID resnet18
Crop Pct 0.875
Image Size 224
Interpolation bilinear
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resnet26

Parameters 16 Million

FLOPs 3 Billion

File Size 61.16 MB

Training Data ImageNet

Training Resources

Training Time

Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID resnet26
Crop Pct 0.875
Image Size 224
Interpolation bicubic
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resnet34

Parameters 22 Million

FLOPs 5 Billion

File Size 83.25 MB

Training Data ImageNet

Training Resources

Training Time

Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID resnet34
Crop Pct 0.875
Image Size 224
Interpolation bilinear
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resnet50

Parameters 26 Million

FLOPs 5 Billion

File Size 97.74 MB

Training Data ImageNet

Training Resources

Training Time

Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID resnet50
Crop Pct 0.875
Image Size 224
Interpolation bicubic
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resnetblur50

Parameters 26 Million

FLOPs 7 Billion

File Size 97.74 MB

Training Data ImageNet

Training Resources

Training Time

Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Blur Pooling
ID resnetblur50
Crop Pct 0.875
Image Size 224
Interpolation bicubic
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tv_resnet101

Parameters 45 Million

FLOPs 10 Billion

File Size 170.45 MB

Training Data ImageNet

Training Resources

Training Time

Training Techniques SGD with Momentum, Weight Decay
Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID tv_resnet101
LR 0.1
Epochs 90
Crop Pct 0.875
LR Gamma 0.1
Momentum 0.9
Batch Size 32
Image Size 224
LR Step Size 30
Weight Decay 0.0001
Interpolation bilinear
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tv_resnet152

Parameters 60 Million

FLOPs 15 Billion

File Size 230.34 MB

Training Data ImageNet

Training Resources

Training Time

Training Techniques SGD with Momentum, Weight Decay
Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID tv_resnet152
LR 0.1
Epochs 90
Crop Pct 0.875
LR Gamma 0.1
Momentum 0.9
Batch Size 32
Image Size 224
LR Step Size 30
Weight Decay 0.0001
Interpolation bilinear
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tv_resnet34

Parameters 22 Million

FLOPs 5 Billion

File Size 83.26 MB

Training Data ImageNet

Training Resources

Training Time

Training Techniques SGD with Momentum, Weight Decay
Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID tv_resnet34
LR 0.1
Epochs 90
Crop Pct 0.875
LR Gamma 0.1
Momentum 0.9
Batch Size 32
Image Size 224
LR Step Size 30
Weight Decay 0.0001
Interpolation bilinear
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tv_resnet50

Parameters 26 Million

FLOPs 5 Billion

File Size 97.75 MB

Training Data ImageNet

Training Resources

Training Time

Training Techniques SGD with Momentum, Weight Decay
Architecture 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax
ID tv_resnet50
LR 0.1
Epochs 90
Crop Pct 0.875
LR Gamma 0.1
Momentum 0.9
Batch Size 32
Image Size 224
LR Step Size 30
Weight Decay 0.0001
Interpolation bilinear
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README.md

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.

How do I load this model?

To load a pretrained model:

import timmm = timm.create_model('resnet18', pretrained=True)m.eval()

Replace the model name with the variant you want to use, e.g. resnet18. You can find the IDs in the model summaries at the top of this page.

How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

Citation

@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}}

Image Classification on ImageNet

Image Classification on ImageNet
MODEL TOP 1 ACCURACY TOP 5 ACCURACY
resnetblur50 79.29% 94.64%
resnet50 79.04% 94.39%
tv_resnet152 78.32% 94.05%
tv_resnet101 77.37% 93.56%
tv_resnet50 76.16% 92.88%
resnet26 75.29% 92.57%
resnet34 75.11% 92.28%
tv_resnet34 73.3% 91.42%
resnet18 69.74% 89.09%
Papers with Code - ResNet (2024)
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