This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from NGC

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

Default Configuration

This model trains in 2500 epochs, under the following setup:

\n
Global Batch Size: 16
\n
\n
Optimizer RMSProp:
\n
- decay: 0.9
- momentum: 0.8
- centered: True
\n
\n
Learning Rate Schedule: Exponential Step Decay
\n
- decay: 0.8
- steps: 500
- initial learning rate: 1e-4
\n
\n
Weight Initialization: He Uniform Distribution (introduced by Kaiming He et al. in 2015 to address issues related ReLU activations in deep neural networks)
\n
\n
Loss Function: Adaptive
\n
- When DICE Loss < 0.3, Loss = Binary Cross Entropy
- Else, Loss = DICE Loss
\n
\n
Data Augmentation
\n
- Random Horizontal Flip (50% chance)
- Random Rotation 90°
\n
\n
Activation Functions:
\n
- ReLU is used for all layers
- Sigmoid is used at the output to ensure that the ouputs are between [0, 1]
\n
\n
Weight decay: 1e-5
\n

Feature support matrix

The following features are supported by this model.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Feature	UNet Medical
Automatic mixed precision (AMP)	Yes
Horovod Multi-GPU (NCCL)	Yes
Accelerated Linear Algebra (XLA)	Yes

Features

Automatic Mixed Precision (AMP)

This implementation of UNet uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.

Horovod

Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.

Multi-GPU training with Horovod

Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the TensorFlow tutorial.

XLA support (experimental)

XLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage: most internal benchmarks run ~1.1-1.5x faster after XLA is enabled.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training previously required two steps:

Porting the model to use the FP16 data type where appropriate.
Adding loss scaling to preserve small gradient values.

This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally.

In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.

How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.

Enabling mixed precision

This implementation exploits the TensorFlow Automatic Mixed Precision feature. In order to enable mixed precision training, the following environment variables must be defined with the correct value before the training starts:

TF_ENABLE_AUTO_MIXED_PRECISION=1\n

Exporting these variables ensures that loss scaling is performed correctly and automatically.\nBy supplying the --amp flag to the main.py script while training in FP32, the following variables are set to their correct value for mixed precision training:

if params.use_amp:\n  os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n

Enabling TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.

TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.

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Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- TensorFlow 20.06-tf1-py3 [NGC container](https://ngc.nvidia.com/registry/nvidia-tensorflow)\n- GPU-based architecture:\n - [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n - [NVIDIA Turing](https://www.nvidia.com/en-us/geforce/turing/)\n - [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\n- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n- [Accessing And Pulling From The NGC container registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)\n- [Running TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)\n \nFor those unable to use the TensorFlow NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the UNet model on the [EM segmentation challenge dataset](http://brainiac2.mit.edu/isbi_challenge/home). These steps enable you to build the UNet TensorFlow NGC container, train and evaluate your model, and generate predictions on the test data. Furthermore, you can then choose to:\n* compare your evaluation accuracy with our [Training accuracy results](#training-accuracy-results),\n* compare your training performance with our [Training performance benchmark](#training-performance-benchmark),\n* compare your inference performance with our [Inference performance benchmark](#inference-performance-benchmark).\n \nFor the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n\n ```bash\n git clone https://github.com/NVIDIA/DeepLearningExamples\n cd DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial\n ```\n\n2. Build the UNet TensorFlow NGC container.\n\n ```bash\n # Build the docker container\n docker build . --rm -t unet_industrial:latest\n ```\n3. Start an interactive session in the NGC container to run preprocessing/training/inference.\n\n ```bash\n # make a directory for the dataset, for example ./dataset\n mkdir \n # make a directory for results, for example ./results\n mkdir \n # start the container with nvidia-docker\n nvidia-docker run -it --rm --gpus all \\\n --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 \\\n -v :/data/ \\\n -v :/results \\\n unet_industrial:latest\n ```\n\n4. Download and preprocess the dataset: DAGM2007\n\n In order to download the dataset. You can execute the following:\n \n ```bash\n ./download_and_preprocess_dagm2007.sh /data\n ```\n\n **Important Information:** Some files of the dataset require an account to be downloaded, the script will invite you to download them manually and put them in the correct directory.\n\n5. Start training.\n\nTo run training for a default configuration (as described in Default configuration, for example 1/4/8 GPUs,\nFP32/TF-AMP), launch one of the scripts in the `./scripts` directory called\n`./scripts/UNet{_AMP}_{1, 4, 8}GPU.sh`\n\nEach of the scripts requires three parameters:\n* path to the results directory of the model as the first argument\n* path to the dataset as a second argument\n* class ID from DAGM used (between 1-10)\n\nFor example, for class 1:\n\n```bash\ncd scripts/\n./UNet_1GPU.sh /results /data 1\n```\n\n6. Run evaluation\n\nModel evaluation on a checkpoint can be launched by running one of the scripts in the `./scripts` directory\ncalled `./scripts/UNet{_AMP}_EVAL.sh`.\n\nEach of the scripts requires three parameters:\n* path to the results directory of the model as the first argument\n* path to the dataset as a second argument\n* class ID from DAGM used (between 1-10)\n\nFor example, for class 1:\n\n```bash\ncd scripts/\n./UNet_EVAL.sh /results /data 1\n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Command line options\n\nTo see the full list of available options and their descriptions, use the -h or --help command line option, for example:\n\n```bash\npython main.py --help\n```\n\nThe following mandatory flags must be used to tune different aspects of the training:\n\ngeneral\n-------\n`--exec_mode=train_and_evaluate` Which execution mode to run the model into.\n\n`--iter_unit=batch` Will the model be run for X batches or X epochs ?\n\n`--num_iter=2500` Number of iterations to run.\n\n`--batch_size=16` Size of each minibatch per GPU.\n\n`--results_dir=/path/to/results` Directory in which to write training logs, summaries and checkpoints.\n\n`--data_dir=/path/to/dataset` Directory which contains the DAGM2007 dataset.\n\n`--dataset_name=DAGM2007` Name of the dataset used in this run (only DAGM2007 is supported atm).\n\n`--dataset_classID=1` ClassID to consider to train or evaluate the network (used for DAGM).\n\nmodel\n-----\n\n`--amp` Enable Automatic Mixed Precision to speedup FP32 computation using tensor cores.\n\n`--xla` Enable TensorFlow XLA to maximise performance.\n\n`--use_auto_loss_scaling` Use AutoLossScaling in TF-AMP\n\n#### Dataset guidelines\n\nThe UNet model was trained with the [Weakly Supervised Learning for Industrial Optical Inspection (DAGM 2007)](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html) dataset.\n\n> The competition is inspired by problems from industrial image processing. In order to satisfy their customers' needs, companies have to guarantee the quality of their products, which can often be achieved only by inspection of the finished product. Automatic visual defect detection has the potential to reduce the cost of quality assurance significantly.\n>\n> The competitors have to design a stand-alone algorithm which is able to detect miscellaneous defects on various background textures.\n>\n> The particular challenge of this contest is that the algorithm must learn, without human intervention, to discern defects automatically from a weakly labeled (i.e., labels are not exact to the pixel level) training set, the exact characteristics of which are unknown at development time. During the competition, the programs have to be trained on new data without any human guidance.\n\n**Source:** https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html\n\n> The provided data is artificially generated, but similar to real world problems. It consists of multiple data sets, each consisting of 1000 images showing the background texture without defects, and of 150 images with one labeled defect each on the background texture. The images in a single data set are very similar, but each data set is generated by a different texture model and defect model.\n\n> Not all deviations from the texture are necessarily defects. The algorithm will need to use the weak labels provided during the training phase to learn the properties that characterize a defect.\n\n> Below are two sample images from two data sets. In both examples, the left images are without defects; the right ones contain a scratch-shaped defect which appears as a thin dark line, and a diffuse darker area, respectively. The defects are weakly labeled by a surrounding ellipse, shown in red.\n\n![DAGM_Examples](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/Segmentation/UNet_Industrial//images/dagm2007.png)\n\n\nThe DAGM2007 challenge comes in propose two different challenges:\n- A development set: public and available for download from\n[here](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html).\nThe number of classes and sub-challenges for the development set is 6.\n- A competition set: which requires an account to be downloaded from [here](https://hci.iwr.uni-heidelberg.de/node/3616).\nThe number of classes and sub-challenges for the competition set is 10.\n\nThe challenge consists in designing a single model with a set of predefined hyper-parameters which will not change\nacross the 10 different classes or sub-challenges of the competition set.\n\nThe performance shall be measured on the competition set which is normalized and more complex that the public dataset\nwhile offering the most unbiased evaluation method.\n\n### Training Process\n\n*Laplace Smoothing*\n\nWe use this technique in the DICE loss to improve the training efficiency. This technique consists in replacing the\nepsilon parameter (used to avoid dividing by zero and very small: +/- 1e-7) by 1. You can find more information on:\n[https://en.wikipedia.org/wiki/Additive_smoothing](https://en.wikipedia.org/wiki/Additive_smoothing)\n\n*Adaptive Loss*\n\nThe DICE Loss is not able to provide a meaningful gradient at initialisation. This leads to a model instability which\noften push the model to diverge. Nonetheless, once the model starts to converge, DICE loss is able to very efficiently\nfully train the model. Therefore, we implemented an *adaptive loss* which is composed of two sub-losses:\n\n- Binary Cross-Entropy (BCE)\n- DICE Loss\n\nThe model is trained with the BCE loss until the DICE Loss reach a experimentally defined threshold (0.3).\nThereafter, DICE loss is used to finish training.\n\n*Weak Labelling*\n\nThis dataset is referred as weakly labelled. That means that the segmentation labels are not given at the pixel level\nbut rather in an approximate fashion.","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\nThe following sections shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark the inference performance, you can run one of the scripts in the `./scripts/benchmarking/` directory\ncalled `./scripts/benchmarking/UNet_trainbench{_AMP}_{1, 4, 8}GPU.sh`.\n\nEach of the scripts requires three parameters:\n* path to the dataset as the first argument\n* class ID from DAGM used (between 1-10)\n\nFor example:\n\n```bash\ncd scripts/benchmarking/\n./UNet_trainbench_1GPU.sh /data 1\n```\n\n#### Inference performance benchmark\n\nTo benchmark the training performance, you can run one of the scripts in the `./scripts/benchmarking/` directory\ncalled `./scripts/benchmarking/UNet_evalbench{_AMP}.sh`.\n\nEach of the scripts requires three parameters:\n* path to the dataset as the first argument\n* class ID from DAGM used (between 1-10)\n\nFor example:\n\n```bash\ncd scripts/benchmarking/\n./UNet_evalbench_AMP.sh /data 1\n```\n\n### Results\n\nThe following sections provide details on the achieved results in training accuracy, performance and inference performance.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n \nOur results were obtained by running the `./scripts/UNet{_AMP}_{1, 8}GPU.sh` training\nscript in the `tensorflow:20.06-tf1-py3` NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.\n\n| GPUs | Batch size / GPU | Accuracy - TF32 | Accuracy - mixed precision | Time to train - TF32 [min] | Time to train - mixed precision [min] | Time to train speedup (TF32 to mixed precision) |\n|:----:|:----------------:|:---------------:|:--------------------------:|:--------------------:|:-------------------------------:|:-----------------------------------------------:|\n| 1 | 16 | 0.9717 | 0.9726 | 3.6 | 2.3 | 1.57 |\n| 8 | 2 | 0.9733 | 0.9683 | 4.3 | 3.5 | 1.23 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `./scripts/UNet{_AMP}_{1, 8}GPU.sh` training\nscript in the `tensorflow:20.06-tf1-py3` NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\n\n| GPUs | Batch size / GPU | Accuracy - FP32 | Accuracy - mixed precision | Time to train - FP32 [min] | Time to train - mixed precision [min] | Time to train speedup (FP32 to mixed precision) |\n|:----:|:----------------:|:---------------:|:--------------------------:|:--------------------:|:-------------------------------:|:-----------------------------------------------:|\n| 1 | 16 | 0.9643 | 0.9653 | 10 | 8 | 1.25 |\n| 8 | 2 | 0.9637 | 0.9655 | 2.5 | 2.5 | 1.00 |\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the scripts\n`./scripts/benchmarking/UNet_trainbench{_AMP}_{1, 4, 8}GPU.sh` training script in the\nTensorFlow `20.06-tf1-py3` NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.\n\n| GPUs | Batch size / GPU | Throughput - TF32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (TF32 - mixed precision) | Strong scaling - TF32 | Strong scaling - mixed precision |\n|:----:|:----------------:|:-------------------------:|:------------------------------------:|:-------------------------------------------:|:---------------------:|:--------------------------------:|\n| 1 | 16 | 135.95 | 255.26 | 1.88 | - | - |\n| 4 | 4 | 420.2 | 691.19 | 1.64 | 3.09 | 2.71 |\n| 8 | 2 | 655.05 | 665.66 | 1.02 | 4.82 | 2.61 |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the scripts\n`./scripts/benchmarking/UNet_trainbench{_AMP}_{1, 4, 8}GPU.sh` training script in the\nTensorFlow `20.06-tf1-py3` NGC container on an NVIDIA DGX-1 (8 V100 16GB) GPUs.\n\n| GPUs | Batch size / GPU | Throughput - FP32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (FP32 - mixed precision) | Strong scaling - FP32 | Strong scaling - mixed precision |\n|:----:|:----------------:|:-------------------------:|:------------------------------------:|:-------------------------------------------:|:---------------------:|:--------------------------------:|\n| 1 | 16 | 86.95 | 168.54 | 1.94 | - | - |\n| 4 | 4 | 287.01 | 459.07 | 1.60 | 3.30 | 2.72 |\n| 8 | 2 | 474.77 | 444.13 | 0.94 | 5.46 | 2.64 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.\n\n#### Inference performance results\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)\n\nOur results were obtained by running the scripts `./scripts/benchmarking/UNet_evalbench{_AMP}.sh`\nevaluation script in the `20.06-tf1-py3` NGC container on NVIDIA DGX A100 (1x A100 40GB) GPUs.\n\nFP16\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 247.83 |\n| 8 | 512x512x1 | 761.41 |\n| 16 | 512x512x1 | 823.46 |\n\nTF32\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 227.97 |\n| 8 | 512x512x1 | 419.70 |\n| 16 | 512x512x1 | 424.57 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nOur results were obtained by running the scripts `./scripts/benchmarking/UNet_evalbench{_AMP}.sh`\nevaluation script in the `20.06-tf1-py3` NGC container on NVIDIA DGX-1 (1x V100 16GB) GPUs.\n\nFP16\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 157.91 |\n| 8 | 512x512x1 | 438.00 |\n| 16 | 512x512x1 | 469.27 |\n\nFP32\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 159.65 |\n| 8 | 512x512x1 | 243.99 |\n| 16 | 512x512x1 | 250.23 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.","hasPerformance","policies",[],{"_77":21,"_79":199,"_170":-5,"_176":31,"_178":31,"_194":198,"_181":87},{"_77":23,"_79":23,"_170":-5,"_176":31,"_178":31,"_194":198,"_181":87},[],"Latest (20.06.0)","nvidia/dle/unet_2d-industrial_tf1","key","nvidia/dle/resources/unet_2d-industrial_tf1","resourceType","resources","resourceId","unet_2d-industrial_tf1","shortDescription","This model is a convolutional neural network for 2D image segmentation tuned to avoid overfitting.","This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/unet2dind-tf1)\n\n\nThis UNet model is adapted from the original version of the [UNet model](https://arxiv.org/abs/1505.04597) 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:\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://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/Segmentation/UNet_Industrial//images/unet.png)\n\n\nFigure 1. Architecture of the UNet Industrial\n\n### Default Configuration\n\nThis model trains in 2500 epochs, under the following setup:\n\n* Global Batch Size: 16\n\n* Optimizer RMSProp:\n * decay: 0.9\n * momentum: 0.8\n * centered: True\n\n* Learning Rate Schedule: Exponential Step Decay\n * decay: 0.8\n * steps: 500\n * initial learning rate: 1e-4\n\n* Weight Initialization: He Uniform Distribution (introduced by [Kaiming He et al. in 2015](https://arxiv.org/abs/1502.01852) to address issues related ReLU activations in deep neural networks)\n\n* Loss Function: Adaptive\n * When DICE Loss < 0.3, Loss = Binary Cross Entropy\n * Else, Loss = DICE Loss\n\n* Data Augmentation\n * Random Horizontal Flip (50% chance)\n * Random Rotation 90°\n\n* Activation Functions:\n * ReLU is used for all layers\n * Sigmoid is used at the output to ensure that the ouputs are between [0, 1]\n\n* Weight decay: 1e-5\n\n### Feature support matrix\n \nThe following features are supported by this model.\n \n| **Feature** | **UNet Medical** |\n|---------------------------------|-----|\n| Automatic mixed precision (AMP) | Yes |\n| Horovod Multi-GPU (NCCL) | Yes |\n| Accelerated Linear Algebra (XLA)| Yes |\n \n#### Features\n \n**Automatic Mixed Precision (AMP)**\n \nThis implementation of UNet uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.\n \n**Horovod**\n \nHorovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the [Horovod: Official repository](https://github.com/horovod/horovod).\n \n**Multi-GPU training with Horovod**\n \nOur model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage).\n \n**XLA support (experimental)**\n \nXLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage: most internal benchmarks run ~1.1-1.5x faster after XLA is enabled.\n \n### Mixed precision training\n \nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n\n#### Enabling mixed precision\n \nThis implementation exploits the TensorFlow Automatic Mixed Precision feature. In order to enable mixed precision training, the following environment variables must be defined with the correct value before the training starts:\n```\nTF_ENABLE_AUTO_MIXED_PRECISION=1\n```\nExporting these variables ensures that loss scaling is performed correctly and automatically.\nBy supplying the `--amp` flag to the `main.py` script while training in FP32, the following variables are set to their correct value for mixed precision training:\n```\nif params.use_amp:\n os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n```\n \n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.","displayName","UNet Industrial for TensorFlow1",["D",1667525203236],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/unet_2d-industrial_tf1","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_108":1417,"_110":1418},"ngcApps",[1370],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1351":1352,"_1353":1354,"_1355":1356,"_1357":1358,"_1359":253,"_139":277,"_1360":1361,"_1362":56,"_1363":87,"_1364":87,"_1365":1366,"_1367":87,"_1368":1369},"amplitude",{"_1348":1349,"_950":-7,"_1350":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1342":1343,"_1344":1345,"_1346":1347},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1225":356,"_1226":1227,"_1228":1229,"_1230":1231},"announcements",[],"consentState",{"_314":1215,"_1216":87,"_1217":31,"_1218":31,"_1219":1220},"consentConfig",{"_296":297,"_298":299,"_300":301,"_302":303,"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315},"showContactAdmin",1784129913736,[],[],[291,292,293,294,295],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_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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