What is Segnet?
- Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-wise Image Segmentation
Segnet = (Encoder + Decoder) + Pixel-Wise Classification layer
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla, Senior Member, IEEE) arXiv:1511.00561v3
What is SegNet-Basic?
- "In order to analyse SegNet and compare its performance with FCN (decoder variants) we use a smaller version of SegNet, termed SegNet-Basic , which ha 4 encoders and 4 decoders. All the encoders in SegNet-Basic perform max-pooling and subsampling and the corresponding decoders upsample its input using the received max-pooling indices."
Basically it's a mini-segnet to experiment / test the architecure with convnets, such as FCN.
Steps To Run The Model:
python model-basic.pyto create
segNet_basic_modelfor keras to use.
model-basic.pycontains the architecure.
In a different directory run this to download the dataset from original Implementation.
git clone firstname.lastname@example.org:alexgkendall/SegNet-Tutorial.git
- copy the
/CamVidto here, or change the
data_loader.pyto the above directory
python data_loader.pyto generate these two files:
- This will make it easy to process the model over and over, rather than waiting the data to be loaded into memory.
[x] SegNet-Basic [ ] SegNet [x] Test Accuracy [ ] Requirements
Segnet-Basic Road Scene Results:
- Train / Test:
Train on 367 samples, validate on 233 samples Epoch 101/102 366/367 [============================>.] - ETA: 0s - loss: 0.3835 - acc: 0.8737Epoch 00000: val_acc improved from -inf to 0.76367, saving model to weights.best.hdf5 367/367 [==============================] - 231s - loss: 0.3832 - acc: 0.8738 - val_loss: 0.7655 - val_acc: 0.7637 Epoch 102/102 366/367 [============================>.] - ETA: 0s - loss: 0.3589 - acc: 0.8809Epoch 00001: val_acc did not improve 367/367 [==============================] - 231s - loss: 0.3586 - acc: 0.8810 - val_loss: 2.4447 - val_acc: 0.4478