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UNet_Industrial TensorFlow checkpoint (AMP)

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UNet_Industrial TensorFlow checkpoint trained with AMP


NVIDIA Deep Learning Examples

Use Case




Latest Version



February 2, 2022


132.53 MB

Model Overview

This model is a convolutional neural network for 2D image segmentation tuned to avoid overfitting.

Model Architecture

This UNet model is adapted from the original version of the UNet model which is a convolutional auto-encoder for 2D image segmentation. UNet was first introduced by Olaf Ronneberger, Philip Fischer, and Thomas Brox in the paper: UNet: Convolutional Networks for Biomedical Image Segmentation.

This work proposes a modified version of UNet, called TinyUNet which performs efficiently and with very high accuracy on the industrial anomaly dataset DAGM2007. TinyUNet, like the original UNet is composed of two parts:

  • an encoding sub-network (left-side)
  • a decoding sub-network (right-side).

It repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling layer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D layer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.

TinyUNet has been introduced to reduce the model capacity which was leading to a high degree of over-fitting on a small dataset like DAGM2007. The complete architecture is presented in the figure below:


Figure 1. Architecture of the UNet Industrial


This model was trained using script available on NGC and in GitHub repo


The following datasets were used to train this model:

  • DAGM2007 - Synthetic dataset for defect detection on textured surfaces.


Performance numbers for this model are available in NGC



This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.