Vision-Beyond-Limits
Published:
About
Extracted and classified buildings based on damage levels from the post-earthquake imagery training set, assigning color codes to each category. Implemented multi-label classification model for accurate classification in our network training process
Methodolgy
We tired solving this problems in 5 steps:
- Get mask
First we need to get mask ready. To do that, we have used skimage and matplotlib libraires on our JSON data to from multi-label masking which will used for traing the model for damage detection.
- Define model
We defined Unet model which takes n_classes, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS as argument. We have used softmax as activation function as in our case its multi-class classification.
- Encode data
Now we will encode our labels using LabelEncoder from sklearn. Encoder add values from 0-5 (as we have 6 classes) to our label. Further split our data set into train image and test image in 9:1 ratio.
- Train model
Next, we complied the model using Adam , optimizer, focal-loss as loss function and accuracy metric and used sample weights as data was imbalanced. We tried different things while training the model like changing the number of epochs , changing the weights that we have defined , also we have changed the image size to see if there is any change in the accuracy or not.
- Test model
After training the model we saved the model to use it while testing. Finally we are ready to test our model, plot accuracy and loss graph and get our values of IoU, Precison and Recall. You can check the results that we got in Results section.