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pytorch-image-models/modelindex/.templates/models/inception-v4.md

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# 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](https://paperswithcode.com/method/inception-v3).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@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}
}
```
<!--
Models:
- Name: inception_v4
Metadata:
FLOPs: 15806527936
Training Data:
- ImageNet
Training Techniques:
- Label Smoothing
- RMSProp
- Weight Decay
Training Resources: 20x NVIDIA Kepler GPUs
Architecture:
- Average Pooling
- Dropout
- Inception-A
- Inception-B
- Inception-C
- Reduction-A
- Reduction-B
- Softmax
File Size: 171082495
Tasks:
- Image Classification
ID: inception_v4
LR: 0.045
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v4.py#L313
In Collection: Inception v4
Collections:
- Name: Inception v4
Paper:
title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
Learning
url: https://papperswithcode.com//paper/inception-v4-inception-resnet-and-the-impact
type: model-index
Type: model-index
-->