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YOLOv1.py
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from tensorflow.keras import datasets, layers, models, activations, losses, optimizers, metrics
def create_yolo():
model = models.Sequential()
# Block1
model.add(layers.Convolution2D(64, (7, 7), strides=(2, 2), input_shape=(448, 448, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
# Block2
model.add(layers.Convolution2D(192, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
# Block3
model.add(layers.Convolution2D(128, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(256, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(256, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(512, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
# Block4
model.add(layers.Convolution2D(256, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(512, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(256, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(512, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(256, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(512, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(256, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(512, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(512, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(1024, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
# Block5
model.add(layers.Convolution2D(512, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(1024, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(512, (1, 1), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(1024, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(1024, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(1024, (3, 3), strides=(2, 2), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
# Block6
model.add(layers.Convolution2D(1024, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
model.add(layers.Convolution2D(1024, (3, 3), padding='same'))
model.add(layers.LeakyReLU(alpha=0.1))
# Last Block
model.add(layers.Flatten())
model.add(layers.Dense(4096))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(7 * 7 * 30))
model.add(layers.Reshape(target_shape=(7, 7, 30)))
return model