Help with Semantic Segmentation

username: misiak
cooked: <p>Hey guys,<br>
I´m dealing with semantic segmentation. Situation is following:<br>
I have images with 3 color depth and corresponding masks for each image. Masks have 5 num. classes (1-5). On my output validation results I´m getting very low accuracy and very high loss rate. Can someone please help me ?</p>
<p>Code is following :</p>
<pre><code class="lang-auto"># -*- coding: utf-8 -*-
"""
Created on Tue Jun 16 17:15:47 2020

@author: Michal
"""

import tensorflow as tf
import os
import random
import numpy as np
 
from tqdm import tqdm 

from skimage.io import imread, imshow
from skimage.transform import resize
import matplotlib.pyplot as plt

seed = 100
np.random.seed = seed

IMG_WIDTH = 128
IMG_HEIGHT = 128
IMG_CHANNELS = 3

TRAIN_PATH = 'dataset/train/'
TEST_PATH = 'dataset/test/'

train_ids = next(os.walk(TRAIN_PATH))[1]
test_ids = next(os.walk(TEST_PATH))[2]
#print(len(test_ids))

X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)
Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH), dtype=np.uint8)

print('\n Resizing training images and masks')
for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):   
    path = TRAIN_PATH + id_
    img = imread(path + '/image/' + id_ + '.jpeg')[:,:,:IMG_CHANNELS] 
    img = resize(img, (IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), mode='constant', preserve_range=True)
    
    X_train[n] = img  #Fill empty X_train with values from img
    
    mask = np.zeros((IMG_HEIGHT, IMG_WIDTH), dtype=np.uint8)
    path = TRAIN_PATH + id_
    mask = imread(path + '/mask/' + id_ + '.png')
    mask = resize(mask, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)
        
    Y_train[n] = mask   

# test images
X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)
sizes_test = []
print('\n Resizing test images') 
for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):
    path = TEST_PATH 
    img = imread(path + id_)
    sizes_test.append([img.shape, img.shape])
    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)
    X_test[n] = img
imshow(X_test[random.randint(0,1)])
plt.show()
print('\n Done!')

#image_x = random.randint(0, len(train_ids))
image_x = random.randint(0, len(train_ids))
imshow(X_train[image_x])
plt.show()
imshow(np.squeeze(Y_train[image_x]), vmin=0, vmax=5)
plt.show()

#Unet model
inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)

#Contraction path
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)

c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
 
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
 
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)
p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)
 
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)

#Expansive path 
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)
 
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)
 
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)
 
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)
c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)
 
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)
 
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

#Model checkpoint
checkpointer = tf.keras.callbacks.ModelCheckpoint('model', verbose=1, save_best_only=True)

callbacks = [
        tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),
        tf.keras.callbacks.TensorBoard(log_dir='logs')]

results = model.fit(X_train, Y_train, validation_split=0.10, batch_size=4, epochs=5, callbacks=callbacks)

idx = random.randint(0, len(X_train))


preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1)
preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)
preds_test = model.predict(X_test, verbose=1)

 
preds_train_t = (preds_train &gt; 0.5).astype(np.uint8)
preds_val_t = (preds_val &gt; 0.5).astype(np.uint8)
preds_test_t = (preds_test &gt; 0.5).astype(np.uint8)


# Perform a sanity check on some random training samples
ix = random.randint(0, len(preds_train_t))
imshow(X_train[ix])
plt.show()
imshow(np.squeeze(Y_train[ix]))
plt.show()
imshow(np.squeeze(preds_train_t[ix]))
plt.show()

# Perform a sanity check on some random validation samples
ix = random.randint(0, len(preds_val_t))
imshow(X_train[int(X_train.shape[0]*0.9):][ix])
plt.show()
imshow(np.squeeze(Y_train[int(Y_train.shape[0]*0.9):][ix]))
plt.show()
imshow(np.squeeze(preds_val_t[ix]))
plt.show()
</code></pre>

username: ptrblck
cooked: <p>It seems you are using TensorFlow and Keras, so you might get a better answer on StackOverflow. <img src="https://discuss.pytorch.org/images/emoji/apple/wink.png?v=9" title=":wink:" class="emoji" alt=":wink:"></p>
<p>While I cannot debug the code, I would generally recommend to try to overfit a small data sample (such as 10 images) and make sure your model is able to learn these samples.</p>