inceptionresnetv2.md

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InceptionResNetv2

Table of Contents:

• Overview
• Models
• Usage
• Papers
• Graph

Overview

InceptionResNetv2 is the second model architecture used as a CNN backbone. After a few trained models using Xception model for this task, we wanted to test a bigger and deeper model to see if there's going to be any performance impact. The InceptionResNetv2 is only slightly slower than the Xception but offers much deeper model (so also an ability to learn more complex and more multi-dimentional dependeces) and about twice as many parameters. This yielded our best model so far, the model_0004_inceptionresnetv2_v3.

Models

This model architecture has been used in the following model lines:

• model_0004_inceptionresnetv2
• model_0005_inceptionresnetv2
• model_0006_inceptionresnetv2
• model_0007_inceptionresnetv2
• model_0008_irv2_data_td
• model_0009_irv2_cr_tl
• model_0010_irv2_tcb

and we are currently replacing it with the Regnets (TBA).

Usage

We're using Keras implementation of the InceptionResNetv2 model without the head and with randomly initialized parameters (with an ability to use Imagenet to initialize parameters):
Import:

from tensorflow.keras.applications import InceptionResNetv2

Usage:

cnn_backbone = InceptionResNetv2(weights="imagenet" if settings['CNN_USE_PRETRAINED_WEIGHTS'] else None, include_top=False, input_shape=model_input['shape'])

Papers

The paper describing the model: https://arxiv.org/pdf/1602.07261.pdf

Graph

Model graph: