Machine Learning Application Benchmark - ResNet101

This is a re-implementation of ResNet101 as described [1] (Configuration: option B has been used) 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_resnet101.h5
• ImageNet (without top layers): imagenet_resnet101_notop.h5

Source file: /models/resnet101_vitis.py

Usage:

models.resnet101_vitis.resnet101_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. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.
[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.