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 <h2>oi</h2>

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