| Training Techniques | Nesterov Accelerated Gradient, Weight Decay, Kaiming Initialization |
|---|---|
| Architecture | 1x1 Convolution, Average Pooling, Batch Normalization, Convolution, Dense Block, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | densenet121 |
| SHOW MORE |
| Training Techniques | Nesterov Accelerated Gradient, Weight Decay, Kaiming Initialization |
|---|---|
| Architecture | 1x1 Convolution, Average Pooling, Batch Normalization, Convolution, Dense Block, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | densenet161 |
| SHOW MORE |
| Training Techniques | Nesterov Accelerated Gradient, Weight Decay, Kaiming Initialization |
|---|---|
| Architecture | 1x1 Convolution, Average Pooling, Batch Normalization, Convolution, Dense Block, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | densenet169 |
| SHOW MORE |
| Training Techniques | Nesterov Accelerated Gradient, Weight Decay, Kaiming Initialization |
|---|---|
| Architecture | 1x1 Convolution, Average Pooling, Batch Normalization, Convolution, Dense Block, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | densenet201 |
| SHOW MORE |
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Dense Block, Dropout, Dense Connections, ReLU, Max Pooling, Softmax, Blur Pooling |
|---|---|
| ID | densenetblur121d |
| SHOW MORE |
| Training Techniques | SGD with Momentum, Weight Decay |
|---|---|
| Architecture | 1x1 Convolution, Average Pooling, Batch Normalization, Convolution, Dense Block, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | tv_densenet121 |
| SHOW MORE |
DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, 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
To load a pretrained model:
import timm
m = timm.create_model('densenet121', pretrained=True)
m.eval()
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.
You can follow the timm recipe scripts for training a new model afresh.
@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}}
}
Image Classification on ImageNet
| MODEL | TOP 1 ACCURACY | TOP 5 ACCURACY |
|---|---|---|
| densenet161 | 77.36% | 93.63% |
| densenet201 | 77.29% | 93.48% |
| densenetblur121d | 76.59% | 93.2% |
| densenet169 | 75.9% | 93.02% |
| densenet121 | 75.56% | 92.65% |
| tv_densenet121 | 74.74% | 92.15% |
