Machine Learning Application Benchmark - VGG16

This is a re-implementation of VGG16 as described in Table 1 ConvNet Configuration D [1] changed to fulfill hardware constraints of the Vitis AI framework for inference on Xilinx FPGAs.

Pretrained weights are available from the model database as follows:

• ImageNet (with top layers): imagenet_vgg16.h5
• ImageNet (without top layers): imagenet_vgg16_notop.h5

Source file: /models/vgg16_vitis.py

Usage:

models.vgg16_vitis.vgg16_vitis(
    input_tensor=None, 
    include_top=True, 
    weight_path=None, 
    return_tensor=False, 
    classes=1000, 
    classifier_activation="softmax"
)

Arguments:

• input_tensor: optional keras layer, like an input tensor.
• include_top: whether to include the top layers or top.
• weight_path: If not none, these weights will be loaded.
• return_tensor: Whether to return the network as tensor or as tf.keras.model (if true, weights will not be loaded).
• classes: By default the number of classes are 1000 (ImageNet). Only important include_top=True.
• classifier_activation: By default softmax (ImageNet). Only important if include_top=True.

Returns:

The CNN architecture as tf.keras.model if return_tensor=False, otherwise as tf.keras.layers.

References:

[1] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations, 2015.
[2] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV), vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.