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\na convolutional auto-encoder for 2D image segmentation. UNet was first introduced by\nOlaf Ronneberger, Philip Fischer, and Thomas Brox in the paper:\nUNet: Convolutional Networks for Biomedical Image Segmentation.

This work proposes a modified version of UNet, called TinyUNet which performs efficiently and with very high accuracy\non the industrial anomaly dataset DAGM2007.\nTinyUNet, 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\nlayer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D\nlayer 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\nsmall dataset like DAGM2007. The complete architecture is presented in the figure below:

$\"UnetModel\"$

Figure 1. Architecture of the UNet Industrial

Training

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

Dataset

The following datasets were used to train this model:

DAGM2007 - Synthetic dataset for defect detection on textured surfaces.

Performance

Performance numbers for this model are available in NGC

References

License

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

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UNet was first introduced by\nOlaf Ronneberger, Philip Fischer, and Thomas Brox in the paper:\n[UNet: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597).\n\nThis work proposes a modified version of UNet, called `TinyUNet` which performs efficiently and with very high accuracy\non the industrial anomaly dataset [DAGM2007](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html).\n*TinyUNet*, like the original *UNet* is composed of two parts:\n- an encoding sub-network (left-side)\n- a decoding sub-network (right-side).\n\nIt repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling\nlayer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D\nlayer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.\n\n`TinyUNet` has been introduced to reduce the model capacity which was leading to a high degree of over-fitting on a\nsmall dataset like DAGM2007. The complete architecture is presented in the figure below:\n\n![UnetModel](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/Segmentation/UNet_Industrial/images/unet.png)\n\n\nFigure 1. Architecture of the UNet Industrial\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:unet_industrial_for_tensorflow) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Industrial)\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [DAGM2007](https://conferences.mpi-inf.mpg.de/dagm/2007/prizes.html) - Synthetic dataset for defect detection on textured surfaces.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:unet_industrial_for_tensorflow/performance)\n\n## References\n- [original paper](https://arxiv.org/abs/1505.04597)\n- [NVIDIA model implementation in NGC](https://ngc.nvidia.com/catalog/resources/nvidia:unet_industrial_for_tensorflow)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Industrial)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Segmentation/UNet_Industrial) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","UNet_Industrial TensorFlow checkpoint (AMP)",["D",1680637351312],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/models/unet_industrial_tf_ckpt_amp","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",138965856,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[517,518],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_484":528,"_520":529,"_522":530,"_524":525},{"_484":519,"_520":521,"_522":523,"_524":525},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_526":527,"_520":521},"category","label","field","platform","Computer Vision","soln_computer_vision",{"_526":531,"_520":529},"technology",{"_387":1717,"_389":1718},"ngcApps",[1670],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1652":1653,"_1654":1655,"_1656":1657,"_1658":1659,"_1660":543,"_414":567,"_1661":1662,"_1650":56,"_1663":366,"_1664":366,"_1665":1666,"_1667":366,"_1668":1669},"amplitude",{"_1648":1649,"_1650":56,"_1239":-7,"_1651":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1642":1643,"_1644":1645,"_1646":1647},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1514":441,"_1515":1516,"_1517":1518,"_1519":1520},"announcements",[],"consentState",{"_605":1504,"_1505":366,"_1506":31,"_1507":31,"_1508":1509},"consentConfig",{"_587":588,"_589":590,"_591":592,"_593":594,"_595":596,"_597":598,"_599":600,"_601":602,"_603":604,"_605":606},"showContactAdmin",1782700198376,[],[],[581,582,583,584,585,586],{"_45":60,"_47":71,"_49":382,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":382,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":382,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":382,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":382,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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