Inception v4

ID	inception_v4
LR	0.045
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Image Size	299
Interpolation	bicubic

Parameters 43 Million

FLOPs 16 Billion

File Size 163.16 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-A, Inception-B, Inception-C, Reduction-A, Reduction-B, Softmax
ID	inception_v4
LR	0.045
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Image Size	299
Interpolation	bicubic
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README.md

Summary

Inception-v4 is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than Inception-v3.

How do I load this model?

To load a pretrained model:

import timm
m = timm.create_model('inception_v4', pretrained=True)
m.eval()

Replace the model name with the variant you want to use, e.g. inception_v4. 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_v4	Top 1 Accuracy	1.01%	# 327
		Top 5 Accuracy	16.85%	# 327