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.
366 lines
10 KiB
366 lines
10 KiB
# DenseNet
|
|
|
|
**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
|
|
|
|
The **DenseNet Blur** variant in this collection by Ross Wightman employs [Blur Pooling](http://www.paperswithcode.com/method/blur-pooling)
|
|
|
|
## How do I use this model on an image?
|
|
To load a pretrained model:
|
|
|
|
```python
|
|
import timm
|
|
model = timm.create_model('densenet121', 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. `densenet121`. 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('densenet121', 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](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
|
|
|
|
## Citation
|
|
|
|
```BibTeX
|
|
@article{DBLP:journals/corr/HuangLW16a,
|
|
author = {Gao Huang and
|
|
Zhuang Liu and
|
|
Kilian Q. Weinberger},
|
|
title = {Densely Connected Convolutional Networks},
|
|
journal = {CoRR},
|
|
volume = {abs/1608.06993},
|
|
year = {2016},
|
|
url = {http://arxiv.org/abs/1608.06993},
|
|
archivePrefix = {arXiv},
|
|
eprint = {1608.06993},
|
|
timestamp = {Mon, 10 Sep 2018 15:49:32 +0200},
|
|
biburl = {https://dblp.org/rec/journals/corr/HuangLW16a.bib},
|
|
bibsource = {dblp computer science bibliography, https://dblp.org}
|
|
}
|
|
```
|
|
|
|
```
|
|
@misc{rw2019timm,
|
|
author = {Ross Wightman},
|
|
title = {PyTorch Image Models},
|
|
year = {2019},
|
|
publisher = {GitHub},
|
|
journal = {GitHub repository},
|
|
doi = {10.5281/zenodo.4414861},
|
|
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
|
|
}
|
|
```
|
|
|
|
<!--
|
|
Type: model-index
|
|
Collections:
|
|
- Name: DenseNet
|
|
Paper:
|
|
Title: Densely Connected Convolutional Networks
|
|
URL: https://paperswithcode.com/paper/densely-connected-convolutional-networks
|
|
Models:
|
|
- Name: densenet121
|
|
In Collection: DenseNet
|
|
Metadata:
|
|
FLOPs: 3641843200
|
|
Parameters: 7980000
|
|
File Size: 32376726
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Average Pooling
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Dense Block
|
|
- Dense Connections
|
|
- Dropout
|
|
- Max Pooling
|
|
- ReLU
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- Kaiming Initialization
|
|
- Nesterov Accelerated Gradient
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: densenet121
|
|
LR: 0.1
|
|
Epochs: 90
|
|
Layers: 121
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Batch Size: 256
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L295
|
|
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenet121_ra-50efcf5c.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 75.56%
|
|
Top 5 Accuracy: 92.65%
|
|
- Name: densenet161
|
|
In Collection: DenseNet
|
|
Metadata:
|
|
FLOPs: 9931959264
|
|
Parameters: 28680000
|
|
File Size: 115730790
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Average Pooling
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Dense Block
|
|
- Dense Connections
|
|
- Dropout
|
|
- Max Pooling
|
|
- ReLU
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- Kaiming Initialization
|
|
- Nesterov Accelerated Gradient
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: densenet161
|
|
LR: 0.1
|
|
Epochs: 90
|
|
Layers: 161
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Batch Size: 256
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L347
|
|
Weights: https://download.pytorch.org/models/densenet161-8d451a50.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 77.36%
|
|
Top 5 Accuracy: 93.63%
|
|
- Name: densenet169
|
|
In Collection: DenseNet
|
|
Metadata:
|
|
FLOPs: 4316945792
|
|
Parameters: 14150000
|
|
File Size: 57365526
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Average Pooling
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Dense Block
|
|
- Dense Connections
|
|
- Dropout
|
|
- Max Pooling
|
|
- ReLU
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- Kaiming Initialization
|
|
- Nesterov Accelerated Gradient
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: densenet169
|
|
LR: 0.1
|
|
Epochs: 90
|
|
Layers: 169
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Batch Size: 256
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L327
|
|
Weights: https://download.pytorch.org/models/densenet169-b2777c0a.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 75.9%
|
|
Top 5 Accuracy: 93.02%
|
|
- Name: densenet201
|
|
In Collection: DenseNet
|
|
Metadata:
|
|
FLOPs: 5514321024
|
|
Parameters: 20010000
|
|
File Size: 81131730
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Average Pooling
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Dense Block
|
|
- Dense Connections
|
|
- Dropout
|
|
- Max Pooling
|
|
- ReLU
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- Kaiming Initialization
|
|
- Nesterov Accelerated Gradient
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: densenet201
|
|
LR: 0.1
|
|
Epochs: 90
|
|
Layers: 201
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Batch Size: 256
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L337
|
|
Weights: https://download.pytorch.org/models/densenet201-c1103571.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 77.29%
|
|
Top 5 Accuracy: 93.48%
|
|
- Name: densenetblur121d
|
|
In Collection: DenseNet
|
|
Metadata:
|
|
FLOPs: 3947812864
|
|
Parameters: 8000000
|
|
File Size: 32456500
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Blur Pooling
|
|
- Convolution
|
|
- Dense Block
|
|
- Dense Connections
|
|
- Dropout
|
|
- Max Pooling
|
|
- ReLU
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
ID: densenetblur121d
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L305
|
|
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenetblur121d_ra-100dcfbc.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 76.59%
|
|
Top 5 Accuracy: 93.2%
|
|
- Name: tv_densenet121
|
|
In Collection: DenseNet
|
|
Metadata:
|
|
FLOPs: 3641843200
|
|
Parameters: 7980000
|
|
File Size: 32342954
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Average Pooling
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Dense Block
|
|
- Dense Connections
|
|
- Dropout
|
|
- Max Pooling
|
|
- ReLU
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: tv_densenet121
|
|
LR: 0.1
|
|
Epochs: 90
|
|
Crop Pct: '0.875'
|
|
LR Gamma: 0.1
|
|
Momentum: 0.9
|
|
Batch Size: 32
|
|
Image Size: '224'
|
|
LR Step Size: 30
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L379
|
|
Weights: https://download.pytorch.org/models/densenet121-a639ec97.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 74.74%
|
|
Top 5 Accuracy: 92.15%
|
|
--> |