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pytorch-image-models/docs/models/.templates/models/regnety.md

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# RegNetY
**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
$$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$
For **RegNetX** authors have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier).
For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
{% 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{radosavovic2020designing,
title={Designing Network Design Spaces},
author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
year={2020},
eprint={2003.13678},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Models:
- Name: regnety_002
Metadata:
FLOPs: 255754236
Epochs: 100
Batch Size: 1024
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 12782926
Tasks:
- Image Classification
ID: regnety_002
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L409
In Collection: RegNetY
- Name: regnety_016
Metadata:
FLOPs: 2070895094
Epochs: 100
Batch Size: 1024
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 45115589
Tasks:
- Image Classification
ID: regnety_016
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L433
In Collection: RegNetY
- Name: regnety_004
Metadata:
FLOPs: 515664568
Epochs: 100
Batch Size: 1024
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 17542753
Tasks:
- Image Classification
ID: regnety_004
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L415
In Collection: RegNetY
- Name: regnety_006
Metadata:
FLOPs: 771746928
Epochs: 100
Batch Size: 1024
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 24394127
Tasks:
- Image Classification
ID: regnety_006
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L421
In Collection: RegNetY
- Name: regnety_008
Metadata:
FLOPs: 1023448952
Epochs: 100
Batch Size: 1024
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 25223268
Tasks:
- Image Classification
ID: regnety_008
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L427
In Collection: RegNetY
- Name: regnety_032
Metadata:
FLOPs: 4081118714
Epochs: 100
Batch Size: 512
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 78084523
Tasks:
- Image Classification
ID: regnety_032
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L439
In Collection: RegNetY
- Name: regnety_080
Metadata:
FLOPs: 10233621420
Epochs: 100
Batch Size: 512
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 157124671
Tasks:
- Image Classification
ID: regnety_080
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L457
In Collection: RegNetY
- Name: regnety_040
Metadata:
FLOPs: 5105933432
Epochs: 100
Batch Size: 512
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 82913909
Tasks:
- Image Classification
ID: regnety_040
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L445
In Collection: RegNetY
- Name: regnety_064
Metadata:
FLOPs: 8167730444
Epochs: 100
Batch Size: 512
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 122751416
Tasks:
- Image Classification
ID: regnety_064
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L451
In Collection: RegNetY
- Name: regnety_120
Metadata:
FLOPs: 15542094856
Epochs: 100
Batch Size: 512
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 207743949
Tasks:
- Image Classification
ID: regnety_120
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L463
In Collection: RegNetY
- Name: regnety_160
Metadata:
FLOPs: 20450196852
Epochs: 100
Batch Size: 512
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 334916722
Tasks:
- Image Classification
ID: regnety_160
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L469
In Collection: RegNetY
- Name: regnety_320
Metadata:
FLOPs: 41492618394
Epochs: 100
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
File Size: 580891965
Tasks:
- Image Classification
ID: regnety_320
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L475
In Collection: RegNetY
Collections:
- Name: RegNetY
Paper:
title: Designing Network Design Spaces
url: https://paperswithcode.com//paper/designing-network-design-spaces
type: model-index
Type: model-index
-->
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