[英]Bounding box prediction on CNN multiple class image classification in python
我對4種類型的特定對象進行了訓練和測試。 我也有csv格式的裝訂框條件/關注區域坐標(x,y,w,h)。 該項目的主要目的是預測測試圖像的類別以及感興趣區域周圍的邊框,並在圖像上打印類別名稱。
我已經基於keras庫應用了CNN模型。 它對測試集的給定圖像進行分類。 為了預測給定測試圖像的邊界框坐標,我應該改變什么?
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
#CNN initializing
classifier= Sequential()
#convolutional layer
classifier.add(Convolution2D(filters = 32, kernel_size=(3,3), data_format= "channels_last", input_shape=(64, 64, 3), activation="relu"))
#Pooling
classifier.add(MaxPooling2D(pool_size=(2,2)))
#addition of second convolutional layer
classifier.add(Convolution2D(filters = 32, kernel_size=(3,3), data_format= "channels_last", activation="relu"))
classifier.add(MaxPooling2D(pool_size=(2,2)))
#step 3 - FLatttening
classifier.add(Flatten())
#step 4 - Full connection layer
classifier.add(Dense(128, input_dim = 11, activation = 'relu'))
#output layer
classifier.add(Dense(units = 4, activation = 'sigmoid'))
#compiling the CNN
classifier.compile(optimizer='adam',loss="categorical_crossentropy",metrics =["accuracy"])
#part 2 -Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('dataset/Train',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
test_set = test_datagen.flow_from_directory('dataset/Test',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
classifier.fit_generator(training_set,
steps_per_epoch =4286/32,
epochs = 25,
validation_data = test_set,
validation_steps = 44/32)
您描述的任務是對象檢測,通常需要更復雜的CNN模型。 檢查https://github.com/fizyr/keras-retinanet以了解著名的神經網絡架構之一。
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