I am using the Image segmentation guide by fchollet to perform semantic segmentation. I have attempted modifying the guide to suit my dataset by labelling the 8-bit img mask values into 1 and 2 like in the Oxford Pets dataset. (which will be subtracted to 0 and 1 in class OxfordPets(keras.utils.Sequence):
)
Question is how do I get the IoU metric of a single class (e.g 1)?
I have tried different metrics suggested by Stack Overflow but most of suggest using MeanIoU which I tried but I have gotten nan loss as a result. Here is an example of a mask after using autocontrast.
PIL.ImageOps.autocontrast(load_img(val_target_img_paths[i]))
The model seems to train well but the accuracy was decreasing over time.
Also, can someone help explain how the metric score can be calculated from y_true
and y_pred
? I don’t quite fully understand when the label value is used in the IoU metric calculation.
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Answer
I had a similar problem back then. I used jaccard_distance_loss
and dice_metric
. They are based on IoU. My task was a binary segmentation, so I guess you might have to modify the code in case you want to use it for a multi-label classification problem.
from keras import backend as K def jaccard_distance_loss(y_true, y_pred, smooth=100): """ Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|) = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|)) The jaccard distance loss is usefull for unbalanced datasets. This has been shifted so it converges on 0 and is smoothed to avoid exploding or disapearing gradient. Ref: https://en.wikipedia.org/wiki/Jaccard_index @url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96 @author: wassname """ intersection = K.sum(K.sum(K.abs(y_true * y_pred), axis=-1)) sum_ = K.sum(K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)) jac = (intersection + smooth) / (sum_ - intersection + smooth) return (1 - jac) * smooth def dice_metric(y_pred, y_true): intersection = K.sum(K.sum(K.abs(y_true * y_pred), axis=-1)) union = K.sum(K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)) # if y_pred.sum() == 0 and y_pred.sum() == 0: # return 1.0 return 2*intersection / union # Example size = 10 y_true = np.zeros(shape=(size,size)) y_true[3:6,3:6] = 1 y_pred = np.zeros(shape=(size,size)) y_pred[3:5,3:5] = 1 loss = jaccard_distance_loss(y_true,y_pred) metric = dice_metric(y_pred,y_true) print(f"loss: {loss}") print(f"dice_metric: {metric}")
loss: 4.587155963302747 dice_metric: 0.6153846153846154