FCN

FCN: Fully Convolutional Networks for Semantic Segmentation

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This paper shows that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. This repo adapts vgg16 networks into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task on PASCAL VOC2012 dataset.

Train PASCAL VOC2012

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Download VOC PASCAL trainval and test data.

$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

Extract all of these tars into one directory and rename them, which should have the following basic structure.

VOC           # path:  /home/yang/dataset/VOC
├── test
|    └──VOCdevkit
|        └──VOC2007 (from VOCtest_06-Nov-2007.tar)
└── train
     └──VOCdevkit
         └──VOC2007 (from VOCtrainval_06-Nov-2007.tar)
         └──VOC2012 (from VOCtrainval_11-May-2012.tar)

Then you need parser_voc.py to parser PASCAL VOC dataset and train it. Finally you can evaluate model by python test.py. The prediction results are expected to save in ./data/prediction. Here is my trained weight FCN8s.h5

$ python parser_voc.py --voc_path /home/yang/dataset/VOC
$ python train.py
Epoch 1/30
6000/6000 [==============================] - 3571s 595ms/step - loss: 1.2374 - accuracy: 0.7233
Epoch 2/30
6000/6000 [==============================] - 3552s 592ms/step - loss: 1.1319 - accuracy: 0.7497
...
Epoch 30/30
6000/6000 [==============================] - 3552s 592ms/step - loss: 0.0811 - accuracy: 0.9797

$ python test.py

Citation

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@Github_Project{TensorFlow2.0-Examples,
  author       = YunYang1994,
  email        = [email protected],
  title        = "FCN: Fully Convolutional Networks for Semantic Segmentation.",
  url          = https://github.com/YunYang1994/TensorFlow2.0-Examples,
  year         = 2019,
}