pytorch-image-models/docs/models/wide-resnet.md

# Wide ResNet

**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).

## How do I use this model on an image?
To load a pretrained model:

```python
import timm
model = timm.create_model('wide_resnet101_2', pretrained=True)
model.eval()
```

To load and preprocess the image:
```python 
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:
```python
import torch
with torch.no_grad():
    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:
```python
# 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. `wide_resnet101_2`. 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](https://rwightman.github.io/pytorch-image-models/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).
```python
model = timm.create_model('wide_resnet101_2', pretrained=True).reset_classifier(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](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.

## Citation

```BibTeX
@article{DBLP:journals/corr/ZagoruykoK16,
  author    = {Sergey Zagoruyko and
               Nikos Komodakis},
  title     = {Wide Residual Networks},
  journal   = {CoRR},
  volume    = {abs/1605.07146},
  year      = {2016},
  url       = {http://arxiv.org/abs/1605.07146},
  archivePrefix = {arXiv},
  eprint    = {1605.07146},
  timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

<!--
Type: model-index
Collections:
- Name: Wide ResNet
  Paper:
    Title: Wide Residual Networks
    URL: https://paperswithcode.com/paper/wide-residual-networks
Models:
- Name: wide_resnet101_2
  In Collection: Wide ResNet
  Metadata:
    FLOPs: 29304929280
    Parameters: 126890000
    File Size: 254695146
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Global Average Pooling
    - Max Pooling
    - ReLU
    - Residual Connection
    - Softmax
    - Wide Residual Block
    Tasks:
    - Image Classification
    Training Data:
    - ImageNet
    ID: wide_resnet101_2
    Crop Pct: '0.875'
    Image Size: '224'
    Interpolation: bilinear
  Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/resnet.py#L802
  Weights: https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 78.85%
      Top 5 Accuracy: 94.28%
- Name: wide_resnet50_2
  In Collection: Wide ResNet
  Metadata:
    FLOPs: 14688058368
    Parameters: 68880000
    File Size: 275853271
    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Convolution
    - Global Average Pooling
    - Max Pooling
    - ReLU
    - Residual Connection
    - Softmax
    - Wide Residual Block
    Tasks:
    - Image Classification
    Training Data:
    - ImageNet
    ID: wide_resnet50_2
    Crop Pct: '0.875'
    Image Size: '224'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/resnet.py#L790
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 81.45%
      Top 5 Accuracy: 95.52%
-->