DenseNet

Densely Connected Convolutional Networks

Abstract

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('densenet121_3rdparty_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('densenet121_3rdparty_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Test Command

Prepare your dataset according to the docs.

Test:

python tools/test.py configs/densenet/densenet121_4xb256_in1k.py https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth

Models and results

Image Classification on ImageNet-1k

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	Config	Download
densenet121_3rdparty_in1k*	From scratch	7.98	2.88	74.96	92.21	config	model
densenet169_3rdparty_in1k*	From scratch	14.15	3.42	76.08	93.11	config	model
densenet201_3rdparty_in1k*	From scratch	20.01	4.37	77.32	93.64	config	model
densenet161_3rdparty_in1k*	From scratch	28.68	7.82	77.61	93.83	config	model

Models with * are converted from the official repo. The config files of these models are only for inference. We haven't reproduce the training results.

Citation

@misc{https://doi.org/10.48550/arxiv.1608.06993,
      doi = {10.48550/ARXIV.1608.06993},
      url = {https://arxiv.org/abs/1608.06993},
      author = {Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q.},
      keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
      title = {Densely Connected Convolutional Networks},
      publisher = {arXiv},
      year = {2016},
      copyright = {arXiv.org perpetual, non-exclusive license}
}