Inception ResNet v2

ID	inception_resnet_v2
LR	0.045
Dropout	0.2
Crop Pct	0.897
Momentum	0.9
Image Size	299
Interpolation	bicubic

Parameters 56 Million

FLOPs 17 Billion

File Size 213.41 MB

Training Data ImageNet

Training Resources 20x NVIDIA Kepler GPUs

Training Time

Training Techniques	RMSProp, Weight Decay, Label Smoothing
Architecture	Average Pooling, Dropout, Inception-ResNet-v2-A, Inception-ResNet-v2-B, Inception-ResNet-v2-C, Inception-ResNet-v2 Reduction-B, Reduction-A, Softmax
ID	inception_resnet_v2
LR	0.045
Dropout	0.2
Crop Pct	0.897
Momentum	0.9
Image Size	299
Interpolation	bicubic
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README.md

Summary

Inception-ResNet-v2 is a convolutional neural architecture that builds on the Inception family of architectures but incorporates residual connections (replacing the filter concatenation stage of the Inception architecture).

How do I load this model?

To load a pretrained model:

import timm
m = timm.create_model('inception_resnet_v2', pretrained=True)
m.eval()

Replace the model name with the variant you want to use, e.g. inception_resnet_v2. 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

@misc{szegedy2016inceptionv4,
      title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning}, 
      author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
      year={2016},
      eprint={1602.07261},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Results

Image Classification on ImageNet

Image Classification

BENCHMARK	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
ImageNet	inception_resnet_v2	Top 1 Accuracy	0.95%	# 330
		Top 5 Accuracy	17.29%	# 330