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65 lines
1.8 KiB
65 lines
1.8 KiB
# Inception v4
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**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).
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{szegedy2016inceptionv4,
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title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
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author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
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year={2016},
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eprint={1602.07261},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Models:
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- Name: inception_v4
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Metadata:
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FLOPs: 15806527936
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Training Data:
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- ImageNet
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Training Techniques:
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- Label Smoothing
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- RMSProp
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- Weight Decay
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Training Resources: 20x NVIDIA Kepler GPUs
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Architecture:
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- Average Pooling
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- Dropout
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- Inception-A
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- Inception-B
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- Inception-C
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- Reduction-A
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- Reduction-B
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- Softmax
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File Size: 171082495
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Tasks:
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- Image Classification
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ID: inception_v4
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LR: 0.045
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '299'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v4.py#L313
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In Collection: Inception v4
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Collections:
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- Name: Inception v4
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Paper:
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title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
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Learning
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url: https://paperswithcode.com//paper/inception-v4-inception-resnet-and-the-impact
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type: model-index
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Type: model-index
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-->
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