You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
130 lines
4.2 KiB
130 lines
4.2 KiB
# (Gluon) SENet
|
|
|
|
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
|
|
|
|
The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
|
|
|
|
## How do I use this model on an image?
|
|
|
|
To load a pretrained model:
|
|
|
|
```py
|
|
>>> import timm
|
|
>>> model = timm.create_model('gluon_senet154', 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. `gluon_senet154`. 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('gluon_senet154', 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{hu2019squeezeandexcitation,
|
|
title={Squeeze-and-Excitation Networks},
|
|
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
|
|
year={2019},
|
|
eprint={1709.01507},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.CV}
|
|
}
|
|
```
|
|
|
|
<!--
|
|
Type: model-index
|
|
Collections:
|
|
- Name: Gloun SENet
|
|
Paper:
|
|
Title: Squeeze-and-Excitation Networks
|
|
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
|
|
Models:
|
|
- Name: gluon_senet154
|
|
In Collection: Gloun SENet
|
|
Metadata:
|
|
FLOPs: 26681705136
|
|
Parameters: 115090000
|
|
File Size: 461546622
|
|
Architecture:
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- Softmax
|
|
- Squeeze-and-Excitation Block
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
ID: gluon_senet154
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L239
|
|
Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 81.23%
|
|
Top 5 Accuracy: 95.35%
|
|
--> |