ID	resnext101_32x8d
LR	0.1
Epochs	100
LR Gamma	0.1
Momentum	0.9
Batch Size	32
LR Step Size	30
Weight Decay	0.0001

ID	resnext50_32x4d
LR	0.1
Epochs	100
LR Gamma	0.1
Momentum	0.9
Batch Size	32
LR Step Size	30
Weight Decay	0.0001

ResNeXt

pytorch / vision

Last updated on Feb 12, 2021

Parameters 89 Million

FLOPs 16 Billion

File Size 339.59 MB

Training Data ImageNet

Training Resources 8x NVIDIA V100 GPUs

Training Time

Training Techniques	Weight Decay, SGD with Momentum
Architecture	1x1 Convolution, Batch Normalization, Convolution, Grouped Convolution, Global Average Pooling, ResNeXt Block, Residual Connection, ReLU, Max Pooling, Softmax
ID	resnext101_32x8d
LR	0.1
Epochs	100
LR Gamma	0.1
Momentum	0.9
Batch Size	32
LR Step Size	30
Weight Decay	0.0001
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Parameters 25 Million

FLOPs 4 Billion

File Size 95.79 MB

Training Data ImageNet

Training Resources 8x NVIDIA V100 GPUs

Training Time

Training Techniques	Weight Decay, SGD with Momentum
Architecture	1x1 Convolution, Batch Normalization, Convolution, Grouped Convolution, Global Average Pooling, ResNeXt Block, Residual Connection, ReLU, Max Pooling, Softmax
ID	resnext50_32x4d
LR	0.1
Epochs	100
LR Gamma	0.1
Momentum	0.9
Batch Size	32
LR Step Size	30
Weight Decay	0.0001
SHOW MORE
SHOW LESS

README.md

Summary

A ResNeXt repeats a building block that aggregates a set of transformations with the same topology. Compared to a ResNet, it exposes a new dimension, cardinality (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width.

How do I load this model?

To load a pretrained model:

import torchvision.models as models
resnext50_32x4d = models.resnext50_32x4d(pretrained=True)

Replace the model name with the variant you want to use, e.g. resnext50_32x4d. You can find the IDs in the model summaries at the top of this page.

To evaluate the model, use the image classification recipes from the library.

python train.py --test-only --model='<model_name>'

How do I train this model?

You can follow the torchvision recipe on GitHub for training a new model afresh.

Citation

@article{DBLP:journals/corr/XieGDTH16,
  author    = {Saining Xie and
               Ross B. Girshick and
               Piotr Doll{\'{a}}r and
               Zhuowen Tu and
               Kaiming He},
  title     = {Aggregated Residual Transformations for Deep Neural Networks},
  journal   = {CoRR},
  volume    = {abs/1611.05431},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.05431},
  archivePrefix = {arXiv},
  eprint    = {1611.05431},
  timestamp = {Mon, 13 Aug 2018 16:45:58 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/XieGDTH16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Results

Image Classification on ImageNet

Image Classification

BENCHMARK	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
ImageNet	ResNeXt-101-32x8d	Top 1 Accuracy	79.31%	# 123
		Top 5 Accuracy	94.53%	# 123
ImageNet	ResNeXt-50-32x4d	Top 1 Accuracy	77.62%	# 176
		Top 5 Accuracy	93.7%	# 176