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https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model
Modification of convolutional neural net "UNET" for image segmentation in Keras framework
https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model
keras pretrained-models segmentation unet unet-image-segmentation
Last synced: 8 days ago
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Modification of convolutional neural net "UNET" for image segmentation in Keras framework
- Host: GitHub
- URL: https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model
- Owner: ZFTurbo
- License: gpl-3.0
- Created: 2017-05-04T17:18:29.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-09-20T22:07:19.000Z (almost 3 years ago)
- Last Synced: 2024-03-20T23:10:33.900Z (3 months ago)
- Topics: keras, pretrained-models, segmentation, unet, unet-image-segmentation
- Language: Python
- Size: 319 KB
- Stars: 212
- Watchers: 15
- Forks: 65
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Lists
- awesome-deeplearning-resources - ZF_UNET_224 Pretrained Model
README
# ZF_UNET_224 Pretrained Model
Modification of convolutional neural net "UNET" for image segmentation in Keras framework
## Requirements
Python 3.*, Keras 2.1, Tensorflow 1.4
## Usage
```python
from zf_unet_224_model import ZF_UNET_224, dice_coef_loss, dice_coef
from keras.optimizers import Adam
model = ZF_UNET_224(weights='generator')
optim = Adam()
model.compile(optimizer=optim, loss=dice_coef_loss, metrics=[dice_coef])
model.fit(...)
```
## Notes
- "ZF_UNET_224" Model based on UNET code from following paper: https://arxiv.org/abs/1505.04597
- This model used to get 2nd place in DSTL competition: https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection
- For training used DICE coefficient: https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- Input shape for model is 224x224 (the same as for other popular CNNs like VGG or ResNet)
- It has 3 input channels (to process standard RGB (BGR) images). You can change it with variable "INPUT_CHANNELS"
- In most cases model ZF_UNET_224 is ok to be used without pretrained weights.
- This code should work fine on both Theano and Tensorflow backends. Code prepared for Keras 2.1, if you need code for Keras 1.2 then use this [link](https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model/tree/b68bbf3a8af4b732a68cf693fcaa59ae19a0e5e5):
## Pretrained weights
Download: [Weights for Tensorflow backend ~123 MB (Keras 2.1, Dice coef: 0.998)](https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model/releases/download/v1.0/zf_unet_224.h5)
Weights were obtained with random image generator (generator code available here: train_infinite_generator.py). See example of images from generator below.
Dice coefficient for pretrained weights: **~0.998**. See history of learning below:
