Do you have any pretrained parameters we can use?

Hi. I did this project for a company so I can not provide you the trained weights. If you use pretrained weights from ImageNet, you can obtain a similar result as I posted. Hello lzccccc Is pretrained weights from imagenet usage is something like this one? We need to set the trainable layer to false or not?

inputs = layers.Input(shape=(cfg().norm_h, cfg().norm_w, 3))
x = layers.Conv2D(64, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv1')(inputs)
x = layers.Conv2D(64, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

# Block 2
x = layers.Conv2D(128, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

# Block 3
x = layers.Conv2D(256, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block3_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

# Block 4
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block4_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

# Block 5
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

x = layers.GlobalAveragePooling2D()(x)

model1 = tf.keras.Model([inputs], [x], name='vgg16_1')
model1.load_weights('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')
model1.summary()

for layer in model1.layers[:]:
  layer.trainable = False

for layer in model1.layers:
  print(layer, layer.trainable)

lzccccc / 3d-bounding-box-estimation-for-autonomous-driving

Do you have any pretrained parameters we can use? #3