Image segmentation with Intersection Over Union (IOU)

If we plot the data it looks like the below

Encoder and Decoder is the common Convolution model for Image segmentation

How Do We Evaluate Semantic Segmentation Models? 

• Intersection Over Union (IOU)
• IOU is a robust measure of segmentation accuracy

Plot predictions vs truth
num_samples = 20
fig = plt.figure(figsize=(10, 2*num_samples))

for i in range(0, 4*num_samples, 4):
    segment_pred = model.predict(np.array([X_test[i,...]]))
    ax = fig.add_subplot(num_samples, 4, i+1, xticks=[], yticks=[])
    segment_truth = y_test[i,:].reshape(im_size, im_size, 2)[:,:,1]
    ax.imshow(X_test[i,:].reshape(im_size, im_size, 3), interpolation='nearest')
    ax = fig.add_subplot(num_samples, 4, i+2, xticks=[], yticks=[])
    ax.imshow(segment_truth, interpolation='nearest')
    ax = fig.add_subplot(num_samples, 4, i+3, xticks=[], yticks=[])
    ax.imshow(segment_pred.reshape(im_size, im_size, 2)[:,:,1], interpolation='nearest')
    ax = fig.add_subplot(num_samples, 4, i+4, xticks=[], yticks=[])
 
    binary_pred = segment_pred.reshape(im_size, im_size, 2)[:,:,1] > 0.5
    ax.imshow(binary_pred, interpolation='nearest')

    binary_pred_flat = binary_pred.reshape(im_size* im_size)
    segment_truth_flat = segment_truth.reshape(im_size* im_size)
 
    intersection = np.sum((binary_pred_flat + segment_truth_flat == 2.0))
    union = np.sum(np.clip((binary_pred_flat + segment_truth_flat), 0.0, 1.0))

    iou = intersection/union
    ax.set_title('IOU = {}'.format(iou))
 
plt.show()

1st column- Original flower
2nd column --Target/Ground truth
3rd column represents Our network predicted
4th column Threshold prediction with IOU

Reference link:
http://www.robots.ox.ac.uk/~vgg/data/flowers/17/