# RexNet

**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.paperswithcode.com/method/relu6).

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

To load a pretrained model:

```py
>>> import timm
>>> model = timm.create_model('rexnet_100', 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. `rexnet_100`. 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('rexnet_100', 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{han2020rexnet,
      title={ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network}, 
      author={Dongyoon Han and Sangdoo Yun and Byeongho Heo and YoungJoon Yoo},
      year={2020},
      eprint={2007.00992},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

<!--
Type: model-index
Collections:
- Name: RexNet
  Paper:
    Title: 'ReXNet: Diminishing Representational Bottleneck on Convolutional Neural
      Network'
    URL: https://paperswithcode.com/paper/rexnet-diminishing-representational
Models:
- Name: rexnet_100
  In Collection: RexNet
  Metadata:
    FLOPs: 509989377
    Parameters: 4800000
    File Size: 19417552
    Architecture:
    - Batch Normalization
    - Convolution
    - Dropout
    - ReLU6
    - Residual Connection
    Tasks:
    - Image Classification
    Training Techniques:
    - Label Smoothing
    - Linear Warmup With Cosine Annealing
    - Nesterov Accelerated Gradient
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 4x NVIDIA V100 GPUs
    ID: rexnet_100
    LR: 0.5
    Epochs: 400
    Dropout: 0.2
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    Image Size: '224'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    Label Smoothing: 0.1
  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L212
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_100-1b4dddf4.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 77.86%
      Top 5 Accuracy: 93.88%
- Name: rexnet_130
  In Collection: RexNet
  Metadata:
    FLOPs: 848364461
    Parameters: 7560000
    File Size: 30508197
    Architecture:
    - Batch Normalization
    - Convolution
    - Dropout
    - ReLU6
    - Residual Connection
    Tasks:
    - Image Classification
    Training Techniques:
    - Label Smoothing
    - Linear Warmup With Cosine Annealing
    - Nesterov Accelerated Gradient
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 4x NVIDIA V100 GPUs
    ID: rexnet_130
    LR: 0.5
    Epochs: 400
    Dropout: 0.2
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    Image Size: '224'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    Label Smoothing: 0.1
  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L218
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_130-590d768e.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 79.49%
      Top 5 Accuracy: 94.67%
- Name: rexnet_150
  In Collection: RexNet
  Metadata:
    FLOPs: 1122374469
    Parameters: 9730000
    File Size: 39227315
    Architecture:
    - Batch Normalization
    - Convolution
    - Dropout
    - ReLU6
    - Residual Connection
    Tasks:
    - Image Classification
    Training Techniques:
    - Label Smoothing
    - Linear Warmup With Cosine Annealing
    - Nesterov Accelerated Gradient
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 4x NVIDIA V100 GPUs
    ID: rexnet_150
    LR: 0.5
    Epochs: 400
    Dropout: 0.2
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    Image Size: '224'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    Label Smoothing: 0.1
  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L224
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_150-bd1a6aa8.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 80.31%
      Top 5 Accuracy: 95.16%
- Name: rexnet_200
  In Collection: RexNet
  Metadata:
    FLOPs: 1960224938
    Parameters: 16370000
    File Size: 65862221
    Architecture:
    - Batch Normalization
    - Convolution
    - Dropout
    - ReLU6
    - Residual Connection
    Tasks:
    - Image Classification
    Training Techniques:
    - Label Smoothing
    - Linear Warmup With Cosine Annealing
    - Nesterov Accelerated Gradient
    - Weight Decay
    Training Data:
    - ImageNet
    Training Resources: 4x NVIDIA V100 GPUs
    ID: rexnet_200
    LR: 0.5
    Epochs: 400
    Dropout: 0.2
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 512
    Image Size: '224'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    Label Smoothing: 0.1
  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L230
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_200-8c0b7f2d.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 81.63%
      Top 5 Accuracy: 95.67%
-->

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