https://github.com/titu1994/DenseNet
DenseNet implementation in Keras
https://github.com/titu1994/DenseNet
bottleneck deep-learning densenet densenet-model keras paper
Last synced: 7 months ago
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DenseNet implementation in Keras
- Host: GitHub
- URL: https://github.com/titu1994/DenseNet
- Owner: titu1994
- License: mit
- Created: 2016-11-08T11:53:02.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2020-06-10T08:17:17.000Z (over 5 years ago)
- Last Synced: 2024-10-13T14:17:01.721Z (about 1 year ago)
- Topics: bottleneck, deep-learning, densenet, densenet-model, keras, paper
- Language: Python
- Size: 19.8 MB
- Stars: 706
- Watchers: 29
- Forks: 294
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-keras : https://github.com/titu1994/DenseNet
- awesome-image-classification - unofficial-keras : https://github.com/titu1994/DenseNet
README
# Dense Net in Keras
DenseNet implementation of the paper [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993v3.pdf) in Keras
Now supports the more efficient DenseNet-BC (DenseNet-Bottleneck-Compressed) networks. Using the DenseNet-BC-190-40 model,
it obtaines state of the art performance on CIFAR-10 and CIFAR-100
# Architecture
DenseNet is an extention to Wide Residual Networks. According to the paper:
```
The lth layer has l inputs, consisting of the feature maps of all preceding convolutional blocks.
Its own feature maps are passed on to all L − l subsequent layers. This introduces L(L+1) / 2 connections
in an L-layer network, instead of just L, as in traditional feed-forward architectures.
Because of its dense connectivity pattern, we refer to our approach as Dense Convolutional Network (DenseNet).
```
It features several improvements such as :
1. Dense connectivity : Connecting any layer to any other layer.
2. Growth Rate parameter Which dictates how fast the number of features increase as the network becomes deeper.
3. Consecutive functions : BatchNorm - Relu - Conv which is from the Wide ResNet paper and improvement from the ResNet paper.
The Bottleneck - Compressed DenseNets offer further performance benefits, such as reduced number of parameters, with similar or better performance.
- Take into consideration the DenseNet-100-12 model, with nearly 7 million parameters against with the DenseNet-BC-100-12, with just 0.8 million parameters.
The BC model achieves 4.51 % error in comparison to the original models' 4.10 % error
- The best original model, DenseNet-100-24 (27.2 million parameters) achieves 3.74 % error, whereas the DenseNet-BC-190-40 (25.6 million parameters) achieves
3.46 % error which is a new state of the art performance on CIFAR-10.
Dense Nets have an architecture which can be shown in the following image from the paper:
# Performance
The accuracy of DenseNet has been provided in the paper, beating all previous benchmarks in CIFAR 10, CIFAR 100 and SVHN
# Usage
Import the `densenet.py` script and use the `DenseNet(...)` method to create a custom DenseNet model with a variety of parameters.
Examples :
```
import densenet
# 'th' dim-ordering or 'tf' dim-ordering
image_dim = (3, 32, 32) or image_dim = (32, 32, 3)
model = densenet.DenseNet(classes=10, input_shape=image_dim, depth=40, growth_rate=12,
bottleneck=True, reduction=0.5)
```
Or, Import a pre-built DenseNet model for ImageNet, with some of these models having pre-trained weights (121, 161 and 169).
Example :
```
import densenet
# 'th' dim-ordering or 'tf' dim-ordering
image_dim = (3, 224, 224) or image_dim = (224, 224, 3)
model = densenet.DenseNetImageNet121(input_shape=image_dim)
```
Weights for the DenseNetImageNet121, DenseNetImageNet161 and DenseNetImageNet169 models are provided ([in the release tab](https://github.com/titu1994/DenseNet/releases)) and will be automatically downloaded when first called. They have been trained on ImageNet. The weights were ported from the repository https://github.com/flyyufelix/DenseNet-Keras.
# Requirements
- Keras
- Theano (weights not tested) / Tensorflow (tested) / CNTK (weights not tested)
- h5Py