# 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).

## How do I use this model on an image?

To load a pretrained model:

```py
>>> import timm
>>> model = timm.create_model('regnety_002', pretrained=True)
>>> model.eval()
```

To load and preprocess the image:

```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform

>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)

>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```

To get the model predictions:

```py
>>> import torch
>>> with torch.inference_mode():
...     out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```

To get the top-5 predictions class names:

```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
...     categories = [s.strip() for s in f.readlines()]

>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
...     print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```

Replace the model name with the variant you want to use, e.g. `regnety_002`. You can find the IDs in the model summaries at the top of this page.

To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.

## How do I finetune this model?

You can finetune any of the pre-trained models just by changing the classifier (the last layer).

```py
>>> model = timm.create_model('regnety_002', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.

## How do I train this model?

You can follow the [timm recipe scripts](../training_script) 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}
}
```

<!--
Type: model-index
Collections:
- Name: RegNetY
  Paper:
    Title: Designing Network Design Spaces
    URL: https://paperswithcode.com/paper/designing-network-design-spaces
Models:
- Name: regnety_002
  In Collection: RegNetY
  Metadata:
    FLOPs: 255754236
    Parameters: 3160000
    File Size: 12782926
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_002
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 1024
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 70.28%
      Top 5 Accuracy: 89.55%
- Name: regnety_004
  In Collection: RegNetY
  Metadata:
    FLOPs: 515664568
    Parameters: 4340000
    File Size: 17542753
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_004
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 1024
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 74.02%
      Top 5 Accuracy: 91.76%
- Name: regnety_006
  In Collection: RegNetY
  Metadata:
    FLOPs: 771746928
    Parameters: 6060000
    File Size: 24394127
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_006
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 1024
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 75.27%
      Top 5 Accuracy: 92.53%
- Name: regnety_008
  In Collection: RegNetY
  Metadata:
    FLOPs: 1023448952
    Parameters: 6260000
    File Size: 25223268
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_008
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 1024
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 76.32%
      Top 5 Accuracy: 93.07%
- Name: regnety_016
  In Collection: RegNetY
  Metadata:
    FLOPs: 2070895094
    Parameters: 11200000
    File Size: 45115589
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_016
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 1024
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 77.87%
      Top 5 Accuracy: 93.73%
- Name: regnety_032
  In Collection: RegNetY
  Metadata:
    FLOPs: 4081118714
    Parameters: 19440000
    File Size: 78084523
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_032
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 82.01%
      Top 5 Accuracy: 95.91%
- Name: regnety_040
  In Collection: RegNetY
  Metadata:
    FLOPs: 5105933432
    Parameters: 20650000
    File Size: 82913909
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_040
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 79.23%
      Top 5 Accuracy: 94.64%
- Name: regnety_064
  In Collection: RegNetY
  Metadata:
    FLOPs: 8167730444
    Parameters: 30580000
    File Size: 122751416
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_064
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 79.73%
      Top 5 Accuracy: 94.76%
- Name: regnety_080
  In Collection: RegNetY
  Metadata:
    FLOPs: 10233621420
    Parameters: 39180000
    File Size: 157124671
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_080
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 79.87%
      Top 5 Accuracy: 94.83%
- Name: regnety_120
  In Collection: RegNetY
  Metadata:
    FLOPs: 15542094856
    Parameters: 51820000
    File Size: 207743949
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_120
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 80.38%
      Top 5 Accuracy: 95.12%
- Name: regnety_160
  In Collection: RegNetY
  Metadata:
    FLOPs: 20450196852
    Parameters: 83590000
    File Size: 334916722
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_160
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 80.28%
      Top 5 Accuracy: 94.97%
- Name: regnety_320
  In Collection: RegNetY
  Metadata:
    FLOPs: 41492618394
    Parameters: 145050000
    File Size: 580891965
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - ReLU
    - Squeeze-and-Excitation Block
    Tasks:
    - Image Classification
    Training Techniques:
    - SGD with Momentum
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 8x NVIDIA V100 GPUs
    ID: regnety_320
    Epochs: 100
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 256
    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
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 80.8%
      Top 5 Accuracy: 95.25%
-->


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