I have trained a model using python detect the colors of the gemstone and have built a CNN.Herewith Iam attaching the code of mine.(Referred https://www.kaggle.com)
import os
import matplotlib.pyplot as plt
import seaborn as sn
import cv2
from random import randint
import numpy as np
CLASSES, gems = [], [] # names of classes, count of images for each class
for root, dirs, files in os.walk('C:/Users/User/Desktop/Research Project/images'):
f = os.path.basename(root) # get class name - Red,Blue etc
if len(files) > 0:
gems.append(len(files))
if f not in CLASSES:
CLASSES.append(f) # add folder name
gems_count = len(CLASSES) # 6 = number of classes
print('{} classes with {} images in total'.format(len(CLASSES), sum(gems)))
img_w, img_h = 220, 220 # width and height of image
train_dir = 'C:/Users/User/Desktop/Project/images/train'
def read_imgs_lbls(_dir):
Images, Labels = [], []
for root, dirs, files in os.walk(_dir):
f = os.path.basename(root) # get class name - Red, Blue, etc
for file in files:
Labels.append(f)
try:
image = cv2.imread(root+'/'+file) # read the image (OpenCV)
image = cv2.resize(image,(int(img_w*1.5), int(img_h*1.5))) # resize the image (images are different sizes)
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # converts an image from BGR color space to HSV
Images.append(image)
except Exception as e:
print(e)
Images = np.array(Images)
return (Images, Labels)
def get_class_index(Labels):
for i, n in enumerate(Labels):
for j, k in enumerate(CLASSES): # foreach CLASSES
if n == k:
Labels[i] = j
Labels = np.array(Labels)
return Labels
Train_Imgs, Train_Lbls = read_imgs_lbls(train_dir)
Train_Lbls = get_class_index(Train_Lbls)
print('Shape of train images: {}'.format(Train_Imgs.shape))
print('Shape of train labels: {}'.format(Train_Lbls.shape))
dim = 5 #5x5 dimension flat plot
f,ax = plt.subplots(dim,dim)
f.subplots_adjust(0,0,2,2)
for i in range(0,dim):
for j in range(0,dim):
rnd_number = randint(0,len(Train_Imgs))
cl = Train_Lbls[rnd_number]
ax[i,j].imshow(Train_Imgs[rnd_number])
ax[i,j].set_title(CLASSES[cl]+': ' + str(cl))
ax[i,j].axis('off')
def edge_and_cut(img):
try:
edges = cv2.Canny(img, img_w, img_h)
if(np.count_nonzero(edges)>edges.size/10000):
pts = np.argwhere(edges>0)
y1,x1 = pts.min(axis=0)
y2,x2 = pts.max(axis=0)
new_img = img[y1:y2, x1:x2] # crop the region
new_img = cv2.resize(new_img,(img_w, img_h)) # Convert back
else:
new_img = cv2.resize(img,(img_w, img_h))
except Exception as e:
print(e)
new_img = cv2.resize(img,(img_w, img_h))
return new_img
def show_cropped(img):
emb_img = img.copy()
edges = cv2.Canny(img, img_w, img_h)
if(np.count_nonzero(edges)>edges.size/10000):
pts = np.argwhere(edges>0)
y1,x1 = pts.min(axis=0)
y2,x2 = pts.max(axis=0)
new_img = img[y1:y2, x1:x2]
edge_size = 1 #replace it with bigger size for larger images
emb_img[y1-edge_size:y1+edge_size, x1:x2] = [255, 0, 0]
emb_img[y2-edge_size:y2+edge_size, x1:x2] = [255, 0, 0]
emb_img[y1:y2, x1-edge_size:x1+edge_size] = [255, 0, 0]
emb_img[y1:y2, x2-edge_size:x2+edge_size] = [255, 0, 0]
new_img = cv2.resize(new_img,(img_w, img_h)) # Convert to primary size
else:
new_img = cv2.resize(img,(img_w, img_h))
fig, ax = plt.subplots(nrows=1, ncols=4, figsize=(10, 10))
ax[0].imshow(img, cmap='gray')
ax[0].set_title('Original Image', fontsize=14)
ax[1].imshow(edges, cmap='gray')
ax[1].set_title('Canny Edges', fontsize=14)
ax[2].imshow(emb_img, cmap='gray')
ax[2].set_title('Bounding Box', fontsize=14)
ax[3].imshow(new_img, cmap='gray')
ax[3].set_title('Cropped', fontsize=14)
for x in range(0,3):
show_cropped(Train_Imgs[randint(0,len(Train_Imgs))])
def crop_images(Imgs):
CroppedImages = np.ndarray(shape=(len(Imgs), img_w, img_h, 3), dtype=np.int)
ind = 0
for im in Imgs:
x = edge_and_cut(im)
CroppedImages[ind] = x
ind += 1
return CroppedImages
Train_Imgs = crop_images(Train_Imgs)
print('Final shape of images in train set: {} '.format(Train_Imgs.shape))
from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(Train_Imgs, Train_Lbls, shuffle = True, test_size = 0.2, random_state = 42)
print('Shape of X_train: {}, y_train: {} '.format(X_train.shape, y_train.shape))
print('Shape of X_val: {}, y_val: {} '.format(X_val.shape, y_val.shape))
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
filters = 32 # the dimensionality of the output space
kernel_size = 3 # length of the 2D convolution window
max_pool = 2
EPOCHS = 70 # while testing you can change it
batch_size = 32 # number of training samples using in each mini batch during GD (gradient descent)
iter_per_epoch = len(X_train) // batch_size # each sample will be passed [iter_per_epoch] times during training
val_per_epoch = len(X_val) // batch_size
model = Sequential()
# first layer
model.add(Conv2D(batch_size, (kernel_size, kernel_size), activation='relu', padding='same', input_shape=(img_w, img_h, 3))) # 32
model.add(MaxPooling2D((max_pool, max_pool))) #reduce the spatial size of incoming features
# second layer
model.add(Conv2D(2*batch_size, (kernel_size, kernel_size), activation='relu', padding='same')) # 64
model.add(MaxPooling2D((max_pool, max_pool)))
# third layer
model.add(Conv2D(4*batch_size, (kernel_size, kernel_size), activation='relu', padding='same')) # 128
model.add(MaxPooling2D((max_pool, max_pool)))
# fourth layer
model.add(Conv2D(4*batch_size, (kernel_size, kernel_size), activation='relu', padding='same')) # 128
model.add(AveragePooling2D(pool_size= (2, 2), strides= (2, 2)))
# fifth layer
model.add(Conv2D(4*batch_size, (kernel_size, kernel_size), activation='relu', padding='same')) # 128
model.add(MaxPooling2D((max_pool, max_pool)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(16*batch_size, activation='relu')) # 512
model.add(Dense(5, activation='softmax'))
model.summary()
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator( # this is the augmentation configuration used for training
rotation_range=25,
zoom_range=0.1,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
horizontal_flip=True
)
val_datagen = ImageDataGenerator() # for val/testing only rescaling function
n = randint(0,len(X_train))
samples = np.expand_dims(X_train[n], 0)
it = train_datagen.flow(samples, batch_size=batch_size)
cols = 7
fig, ax = plt.subplots(nrows=1, ncols=cols, figsize=(15, 10))
ax[0].imshow(X_train[n], cmap='hsv')
ax[0].set_title('Original', fontsize=10)
for i in range(1,cols):
batch = it.next() # generate batch of images
image = batch[0].astype('uint32') # convert to unsigned int for viewing
ax[i].set_title('augmented {}'.format(i), fontsize=10)
ax[i].imshow(image, cmap='hsv')
train_gen = train_datagen.flow(X_train, y_train, batch_size=batch_size)
val_gen = val_datagen.flow(X_val, y_val, batch_size=batch_size)
m = model.fit_generator(
train_gen,
steps_per_epoch= iter_per_epoch,
epochs=EPOCHS,
validation_data = val_gen,
validation_steps = val_per_epoch,
verbose = 1 # Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
)
score = model.evaluate_generator(val_gen, steps= len(val_gen))
for idx, metric in enumerate(model.metrics_names):
print('{}:{}'.format(metric, score[idx]))
from sklearn.metrics import confusion_matrix
y_pre_test=model.predict(X_val)
y_pre_test=np.argmax(y_pre_test,axis=1)
cm=confusion_matrix(y_val,y_pre_test)
plt.figure(figsize = (5,5))
sn.heatmap(cm, annot=True)
x=(y_pre_test-y_val!=0).tolist()
x=[i for i,l in enumerate(x) if l!=False]
fig,ax=plt.subplots(1,5,sharey=False,figsize=(13,13))
fig.tight_layout()
for i in range(5):
ax[i].imshow(X_val[x[i]][:,:,1])
ax[i].set_xlabel('{}, Pred: {}'.format(CLASSES[y_val[x[i]]],CLASSES[y_pre_test[x[i]]]))
model.save('model_colordetection.h5')
test_dir = 'C:/Users/User/Desktop/Project/images/test/'
Test_Imgs, Test_Lbls = read_imgs_lbls(test_dir)
Test_Lbls = get_class_index(Test_Lbls)
Test_Imgs = crop_images(Test_Imgs)
print('shape of images in test set: {} '.format(Test_Imgs.shape))
f,ax = plt.subplots(3,3)
f.subplots_adjust(0,0,2,2)
for i in range(0,3,1):
for j in range(0,3,1):
rnd_number = randint(0,len(Test_Imgs))
pred_image = np.array([Test_Imgs[rnd_number]])
pred_class = model.predict_classes(pred_image)[0]
pred_prob = model.predict(pred_image).reshape(5)
act = CLASSES[Test_Lbls[rnd_number]]
ax[i,j].imshow(Test_Imgs[rnd_number])
ax[i,j].imshow(pred_image[0])
if(CLASSES[pred_class] != CLASSES[Test_Lbls[rnd_number]]):
t = '{} [{}]'.format(CLASSES[pred_class], CLASSES[Test_Lbls[rnd_number]])
ax[i,j].set_title(t, fontdict={'color': 'darkred'})
else:
t = '[OK] {}'.format(CLASSES[pred_class])
ax[i,j].set_title(t)
ax[i,j].axis('off')
I have got the data set from https://www.kaggle.com/datasets/lsind18/gemstones-images. I am using only 161 images for training process.(I have taken only the gemstones which meets my requirment).
Below are the loss and accuracy rate of the model.
loss:0.8286617994308472
accuracy:0.7878788113594055
Can anyone please let me know how can I increase the accuracy of the trained model?(other than increasing the number of datasets).I want to achieve an accuracy rate of at least 90%
Any help is appreciated.Thank you.
---Updated---
Summary of number of images in train and test folders.
I recommend you consider the use of two Keras callbacks, ReduceLROnPlateau and EarlyStopping. Documentation for each is here and here. Both callbacks should be configured to monitor validation loss. My suggested code for each is shown below.
in model.fit set epochs to a fairly large value like 50. Also in model.fit set the parameter callbacks=callbacks. If estop is activated it will halt training and your model is returned with the weights from the epoch with the lowest validation loss. Reducing the learning rate each time the validation loss increases generally results in achieving a lower validation loss during training. The metrics you are focused on are important but since you are doing multiclass classification you should also consider creating a confusion matrix and a classification score. Apparently you have 5 classes. You should look at the balance of training images. In other words for each class how many image samples are there? Your raining set may be highly imbalanced. For example if one class has say 80 images samples while another has say only 10 then your model will ten to be biased toward the class with the most samples. There are a couple of ways to deal with this. I find the most effective way is to create augmented images for each class that is under represented and add these augment images to the train set. A less effective way is to use the class_weight parameter in model.fit by creating a class weight dictionary.