tensorflow - 如何从 keras 获取对象位置和预测分数

Question

我正在尝试将汽车类型预测为 SUV/卡车/BUS

但是，当我运行 model.predict 时，我得到的只是预测，没有对象位置。

如何从图像中获取预测分数和对象位置。

我已经尝试过 keras 中提供的示例供我学习。

    #Model to identify images
    
    def make_model(input_shape, num_classes):
        inputs = keras.Input(shape=input_shape)
        # Image augmentation block
        x = data_augmentation(inputs)
    
        # Entry block
        x = layers.experimental.preprocessing.Rescaling(1.0 / 255)(x)
        x = layers.Conv2D(32, 3, strides=2, padding="same")(x)
        x = layers.BatchNormalization()(x)
        x = layers.Activation("relu")(x)
    
        x = layers.Conv2D(64, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)
        x = layers.Activation("relu")(x)
    
        previous_block_activation = x  # Set aside residual
    
        for size in [128, 256, 512, 728]:
            x = layers.Activation("relu")(x)
            x = layers.SeparableConv2D(size, 3, padding="same")(x)
            x = layers.BatchNormalization()(x)
    
            x = layers.Activation("relu")(x)
            x = layers.SeparableConv2D(size, 3, padding="same")(x)
            x = layers.BatchNormalization()(x)
    
            x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
    
            # Project residual
            residual = layers.Conv2D(size, 1, strides=2, padding="same")(
                previous_block_activation
            )
            x = layers.add([x, residual])  # Add back residual
            previous_block_activation = x  # Set aside next residual
    
        x = layers.SeparableConv2D(1024, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)
        x = layers.Activation("relu")(x)
    
        x = layers.GlobalAveragePooling2D()(x)
        if num_classes == 2:
            activation = "sigmoid"
            units = 1
        else:
            activation = "softmax"
            units = num_classes
    
        x = layers.Dropout(0.5)(x)
        outputs = layers.Dense(units, activation=activation)(x)
        print(x)
        print(outputs)
        return keras.Model(inputs, outputs)
    
    
    model = make_model(input_shape=image_size + (3,), num_classes=2)
    keras.utils.plot_model(model, show_shapes=True)


img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)  # Create batch axis
predictions = model.predict(img_array)[0]

标签： tensorflowkeras