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pytorch-image-models/hfdocs/source/models/rexnet.mdx

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# 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.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:
```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](../scripts) 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%
-->