DeepLearningExamples/TensorFlow/Segmentation/UNet_Medical

U-Net Medical Image Segmentation for TensorFlow 1.x

This repository provides a script and recipe to train U-Net Medical to achieve state of the art accuracy, and is tested and maintained by NVIDIA.

Table of contents

• Model overview
  • Model architecture
  • Default configuration
  • Feature support matrix
    • Features
  • Mixed precision training
    • Enabling mixed precision
• Setup
  • Requirements
• Quick Start Guide
• Advanced
  • Scripts and sample code
  • Parameters
  • Command-line options
  • Getting the data
    • Dataset guidelines
    • Multi-dataset
  • Training process
  • Inference process
• Performance
  • Benchmarking
    • Training performance benchmark
    • Inference performance benchmark
  • Results
    • Training accuracy results
      • Training accuracy: NVIDIA DGX-1 (8x V100 16G)
    • Training performance results
      • Training performance: NVIDIA DGX-1 (8x V100 16G)
    • Inference performance results
      • Inference performance: NVIDIA DGX-1 (1x V100 16G)
• Release notes
  • Changelog
  • Known issues

Model overview

The U-Net model is a convolutional neural network for 2D image segmentation. This repository contains a U-Net implementation as described in the original paper U-Net: Convolutional Networks for Biomedical Image Segmentation, without any alteration.

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta and Turing GPUs. Therefore, researchers can get results 2.2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

U-Net was first introduced by Olaf Ronneberger, Philip Fischer, and Thomas Brox in the paper: U-Net: Convolutional Networks for Biomedical Image Segmentation. U-Net allows for seamless segmentation of 2D images, with high accuracy and performance, and can be adapted to solve many different segmentation problems.

The following figure shows the construction of the U-Net model and its different components. U-Net is composed of a contractive and an expanding path, that aims at building a bottleneck in its centermost part through a combination of convolution and pooling operations. After this bottleneck, the image is reconstructed through a combination of convolutions and upsampling. Skip connections are added with the goal of helping the backward flow of gradients in order to improve the training.

Default configuration

U-Net consists of a contractive (left-side) and expanding (right-side) path. It repeatedly applies unpadded convolutions followed by max pooling for downsampling. Every step in the expanding path consists of an upsampling of the feature maps and a concatenation with the correspondingly cropped feature map from the contractive path.

Feature support matrix

The following features are supported by this model.

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

Features

Automatic Mixed Precision (AMP)

This implementation of U-Net 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 the Volta and Turing architecture, 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 requires two steps:

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

The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.

For information about:

• 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

Exporting these variables ensures that loss scaling is performed correctly and automatically. By supplying the --use_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:
  os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'

Setup

The following section lists the requirements in order to start training the U-Net Medical model.

Requirements

This repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:

• NVIDIA Docker
• TensorFlow 20.02-tf1-py3 NGC container
• NVIDIA Volta GPU or Turing based GPU

For 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:

• Getting Started Using NVIDIA GPU Cloud
• Accessing And Pulling From The NGC container registry
• Running TensorFlow

For 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.

Quick Start Guide

To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the U-Net model on the EM segmentation challenge dataset. These steps enable you to build the U-Net TensorFlow NGC container, train and evaluate your model, and generate predictions on the test data. Furthermore, you can then choose to:

• compare your evaluation accuracy with our Training accuracy results,
• compare your training performance with our Training performance benchmark,
• compare your inference performance with our Inference performance benchmark.

For the specifics concerning training and inference, see the Advanced section.

1. Clone the repository.

   Executing this command will create your local repository with all the code to run U-Net.

   git clone https://github.com/NVIDIA/DeepLearningExamples
   cd DeepLearningExamples/TensorFlow/Segmentation/U-Net_Medical_TF
   
   

2. Build the U-Net TensorFlow NGC container.

   This command will use the Dockerfile to create a Docker image named unet_tf, downloading all the required components automatically.

   docker build -t unet_tf .
   

   The NGC container contains all the components optimized for usage on NVIDIA hardware.

3. Start an interactive session in the NGC container to run preprocessing/training/inference.

   The following command will launch the container and mount the ./data directory as a volume to the /data directory inside the container, and ./results directory to the /results directory in the container.

   mkdir data
   mkdir results
   docker run --runtime=nvidia -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --rm --ipc=host -v ${PWD}/data:/data -v ${PWD}/results:/results unet_tf:latest /bin/bash
   

   Any datasets and experiment results (logs, checkpoints, etc.) saved to /data or /results will be accessible in the ./data or ./results directory on the host, respectively.

4. Download and preprocess the data.

   The U-Net script main.py operates on data from the ISBI Challenge, the dataset originally employed in the U-Net paper.

   The script download_dataset.py is provided for data download. It is possible to select the destination folder when downloading the files by using the --data_dir flag. For example:

   python download_dataset.py --data_dir /data
   

   Training and test data are composed of 3 multi-page TIF files, each containing 30 2D-images (around 30 Mb total). Once downloaded, the data with the download_dataset.py script can be used to run the training and benchmark scripts described below, by pointing main.py to its location using the --data_dir flag.

   Note: Masks are only provided for training data.

5. Start training.

   After the Docker container is launched, the training with the default hyperparameters (for example 1/8 GPUs FP32/TF-AMP) can be started with:

   bash examples/unet_{FP32, TF-AMP}_{1,8}GPU.sh <path/to/dataset> <path/to/checkpoint>
   

   For example, to run with full precision (FP32) on 1 GPU from the project’s folder, simply use:

   bash examples/unet_FP32_1GPU.sh /data /results
   

   This script will launch a training on a single fold and store the model’s checkpoint in <path/to/checkpoint> directory.

   The script can be run directly by modifying flags if necessary, especially the number of GPUs, which is defined after the -np flag. Since the test volume does not have labels, 20% of the training data is used for validation in 5-fold cross-validation manner. The number of fold can be changed using --crossvalidation_idx with an integer in range 0-4. For example, to run with 4 GPUs using fold 1 use:

   horovodrun -np 4 python main.py --data_dir /data --model_dir /results --batch_size 1 --exec_mode train --crossvalidation_idx 1 --use_xla --use_amp
   

   Training will result in a checkpoint file being written to ./results on the host machine.

6. Start validation/evaluation.

   The trained model can be evaluated by passing the --exec_mode evaluate flag. Since evaluation is carried out on a validation dataset, the --crossvalidation_idx parameter should be filled. For example:

   python main.py --data_dir /data --model_dir /results --batch_size 1 --exec_mode evaluate --crossvalidation_idx 0 --use_xla --use_amp
   

   Evaluation can also be triggered jointly after training by passing the --exec_mode train_and_evaluate flag.

7. Start inference/predictions. To run inference on a checkpointed model, run:

   bash examples/unet_INFER_{FP32, TF-AMP}.sh <path/to/dataset> <path/to/checkpoint>
   

   For example:

   bash examples/unet_INFER_FP32.sh /data /results
   

   Now that you have your model trained and evaluated, you can choose to compare your training results with our Training accuracy results. You can also choose to benchmark the performance of your training Training performance benchmark, or Inference performance benchmark. Following the steps in these sections will ensure that you achieve the same accuracy and performance results as stated in the Results section.

Advanced

The following sections provide greater details of the dataset, running training and inference, and the training results.

Scripts and sample code

In the root directory, the most important files are:

• main.py: Serves as the entry point to the application.
• Dockerfile: Container with the basic set of dependencies to run U-Net.
• requirements.txt: Set of extra requirements for running U-Net.
• download_data.py: Automatically downloads the dataset for training.

The utils/ folder encapsulates the necessary tools to train and perform inference using U-Net. Its main components are:

• cmd_util.py: Implements the command-line arguments parsing.
• data_loader.py: Implements the data loading and augmentation.
• model_fn.py: Implements the logic for training and inference.
• hooks/training_hook.py: Collects different metrics during training.
• hooks/profiling_hook.py: Collects different metrics to be used for benchmarking and testing.
• parse_results.py: Implements the intermediate results parsing.

The model/ folder contains information about the building blocks of U-Net and the way they are assembled. Its contents are:

• layers.py: Defines the different blocks that are used to assemble U-Net
• unet.py: Defines the model architecture using the blocks from the layers.py script

Other folders included in the root directory are:

• dllogger/: Contains the utils for logging
• examples/: Provides examples for training and benchmarking U-Net
• images/: Contains a model diagram

Parameters

The complete list of the available parameters for the main.py script contains:

• --exec_mode: Select the execution mode to run the model (default: train). Modes available:
  • evaluate - loads checkpoint (if available) and performs evaluation on validation subset (requires --crossvalidation_idx other than None).
  • train_and_evaluate - trains model from scratch and performs validation at the end (requires --crossvalidation_idx other than None).
  • predict - loads checkpoint (if available) and runs inference on the test set. Stores the results in --model_dir directory.
  • train_and_predict - trains model from scratch and performs inference.
• --model_dir: Set the output directory for information related to the model (default: /results).
• --log_dir: Set the output directory for logs (default: None).
• --data_dir: Set the input directory containing the dataset (default: None).
• --batch_size: Size of each minibatch per GPU (default: 1).
• --crossvalidation_idx: Selected fold for cross-validation (default: None).
• --max_steps: Maximum number of steps (batches) for training (default: 1000).
• --seed: Set random seed for reproducibility (default: 0).
• --weight_decay: Weight decay coefficient (default: 0.0005).
• --log_every: Log performance every n steps (default: 100).
• --learning_rate: Model’s learning rate (default: 0.0001).
• --augment: Enable data augmentation (default: False).
• --benchmark: Enable performance benchmarking (default: False). If the flag is set, the script runs in a benchmark mode - each iteration is timed and the performance result (in images per second) is printed at the end. Works for both train and predict execution modes.
• --warmup_steps: Used during benchmarking - the number of steps to skip (default: 200). First iterations are usually much slower since the graph is being constructed. Skipping the initial iterations is required for a fair performance assessment.
• --use_xla: Enable accelerated linear algebra optimization (default: False).
• --use_amp: Enable automatic mixed precision (default: False).

Command line options

To see the full list of available options and their descriptions, use the -h or --help command-line option, for example:

python main.py --help

The following example output is printed when running the model:

usage: main.py [-h]
              [--exec_mode {train,train_and_predict,predict,evaluate,train_and_evaluate}]
              [--model_dir MODEL_DIR] --data_dir DATA_DIR [--log_dir LOG_DIR]
              [--batch_size BATCH_SIZE] [--learning_rate LEARNING_RATE]
              [--crossvalidation_idx CROSSVALIDATION_IDX]
              [--max_steps MAX_STEPS] [--weight_decay WEIGHT_DECAY]
              [--log_every LOG_EVERY] [--warmup_steps WARMUP_STEPS]
              [--seed SEED] [--augment] [--no-augment] [--benchmark]
              [--no-benchmark] [--use_amp] [--use_xla]
 
U-Net-medical
 
optional arguments:
 -h, --help            show this help message and exit
 --exec_mode {train,train_and_predict,predict,evaluate,train_and_evaluate}
                       Execution mode of running the model
 --model_dir MODEL_DIR
                       Output directory for information related to the model
 --data_dir DATA_DIR   Input directory containing the dataset for training
                       the model
 --log_dir LOG_DIR     Output directory for training logs
 --batch_size BATCH_SIZE
                       Size of each minibatch per GPU
 --learning_rate LEARNING_RATE
                       Learning rate coefficient for AdamOptimizer
 --crossvalidation_idx CROSSVALIDATION_IDX
                       Chosen fold for cross-validation. Use None to disable
                       cross-validation
 --max_steps MAX_STEPS
                       Maximum number of steps (batches) used for training
 --weight_decay WEIGHT_DECAY
                       Weight decay coefficient
 --log_every LOG_EVERY
                       Log performance every n steps
 --warmup_steps WARMUP_STEPS
                       Number of warmup steps
 --seed SEED           Random seed
 --augment             Perform data augmentation during training
 --no-augment
 --benchmark           Collect performance metrics during training
 --no-benchmark
 --use_amp             Train using TF-AMP
 --use_xla             Train using XLA

The U-Net model was trained in the EM segmentation challenge dataset. Test images provided by the organization were used to produce the resulting masks for submission. Upon registration, the challenge's data is made available through the following links:

• train-volume.tif
• train-labels.tif
• train-volume.tif

Training and test data are comprised of three 512x512x30 TIF volumes (test-volume.tif, train-volume.tif and train-labels.tif). Files test-volume.tif and train-volume.tif contain grayscale 2D slices to be segmented. Additionally, training masks are provided in train-labels.tif as a 512x512x30 TIF volume, where each pixel has one of two classes:

• 0 indicating the presence of cellular membrane,
• 1 corresponding to background.

The objective is to produce a set of masks that segment the data as accurately as possible. The results are expected to be submitted as a 32-bit TIF 3D image, with values between 0 (100% membrane certainty) and 1 (100% non-membrane certainty).

Dataset guidelines

The training and test datasets are given as stacks of 30 2D-images provided as a multi-page TIF that can be read using the Pillow library and NumPy (both Python packages are installed by the Dockerfile).

Initially, data is loaded from a multi-page TIF file and converted to 512x512x30 NumPy arrays with the use of Pillow. The process of loading, normalizing and augmenting the data contained in the dataset can be found in the data_loader.py script.

These NumPy arrays are fed to the model through tf.data.Dataset.from_tensor_slices(), in order to achieve high performance.

The voxel intensities then normalized to an interval [-1, 1], whereas labels are one-hot encoded for their later use in dice or pixel-wise cross-entropy loss, becoming 512x512x30x2 tensors.

If augmentation is enabled, the following set of augmentation techniques are applied:

• Random horizontal flipping
• Random vertical flipping
• Crop to a random dimension and resize to input dimension
• Random brightness shifting

In the end, images are reshaped to 388x388 and padded to 572x572 to fit the input of the network. Masks are only reshaped to 388x388 to fit the output of the network. Moreover, pixel intensities are clipped to the [-1, 1] interval.

Multi-dataset

This implementation is tuned for the EM segmentation challenge dataset. Using other datasets is possible, but might require changes to the code (data loader) and tuning some hyperparameters (e.g. learning rate, number of iterations).

In the current implementation, the data loader works with NumPy arrays by loading them at the initialization, and passing them for training in slices by tf.data.Dataset.from_tensor_slices(). If you’re able to fit your dataset into the memory, then convert the data into three NumPy arrays - training images, training labels, and testing images (optional). If your dataset is large, you will have to adapt the optimizer for the lazy-loading of data. For a walk-through, check the TensorFlow tf.data API guide

The performance of the model depends on the dataset size. Generally, the model should scale better for datasets containing more data. For a smaller dataset, you might experience lower performance.

Training process

The model trains for a total 40,000 batches (40,000 / number of GPUs), with the default U-Net setup:

• Adam optimizer with learning rate of 0.0001.

This default parametrization is applied when running scripts from the ./examples directory and when running main.py without explicitly overriding these parameters. By default, the training is in full precision. To enable AMP, pass the --use_amp flag. AMP can be enabled for every mode of execution.

The default configuration minimizes a function L = 1 - DICE + cross entropy during training.

The training can be run directly without using the predefined scripts. The name of the training script is main.py. Because of the multi-GPU support, training should always be run with the Horovod distributed launcher like this:

horovodrun -np <number/of/gpus> python main.py --data_dir /data [other parameters]

Note: When calling the main.py script manually, data augmentation is disabled. In order to enable data augmentation, use the --augment flag in your invocation.

The main result of the training are checkpoints stored by default in ./results/ on the host machine, and in the /results in the container. This location can be controlled by the --model_dir command-line argument, if a different location was mounted while starting the container. In the case when the training is run in train_and_predict mode, the inference will take place after the training is finished, and inference results will be stored to the /results directory.

If the --exec_mode train_and_evaluate parameter was used, and if --crossvalidation_idx parameter is set to an integer value of {0, 1, 2, 3, 4}, the evaluation of the validation set takes place after the training is completed. The results of the evaluation will be printed to the console.

Inference process

Inference can be launched with the same script used for training by passing the --exec_mode predict flag:

python main.py --exec_mode predict --data_dir /data --model_dir <path/to/checkpoint> [other parameters]

The script will then:

• Load the checkpoint from the directory specified by the <path/to/checkpoint> directory (/results),
• Run inference on the test dataset,
• Save the resulting binary masks in a TIF format.

Performance

Benchmarking

The following section shows how to run benchmarks measuring the model performance in training and inference modes.

Training performance benchmark

To benchmark training, run one of the TRAIN_BENCHMARK scripts in ./examples/:

bash examples/unet_TRAIN_BENCHMARK_{FP32, TF-AMP}_{1, 8}GPU.sh <path/to/dataset> <path/to/checkpoints> <batch/size>

For example, to benchmark training using mixed-precision on 8 GPUs use:

bash examples/unet_TRAIN_BENCHMARK_TF-AMP_8GPU.sh <path/to/dataset> <path/to/checkpoints> <batch/size>

Each of these scripts will by default run 200 warm-up iterations and benchmark the performance during training in the next 800 iterations.

To have more control, you can run the script by directly providing all relevant run parameters. For example:

horovodrun -np <num/of/gpus> python main.py --exec_mode train --benchmark --augment --data_dir <path/to/dataset> --model_dir <optional, path/to/checkpoint> --batch_size <batch/size> --warmup_steps <warm-up/steps> --max_steps <max/steps>

At the end of the script, a line reporting the best train throughput will be printed.

Inference performance benchmark

To benchmark inference, run one of the scripts in ./examples/:

bash examples/unet_INFER_BENCHMARK_{FP32, TF-AMP}.sh <path/to/dataset> <path/to/checkpoints> <batch/size>

For example, to benchmark inference using mixed-precision:

bash examples/unet_INFER_BENCHMARK_TF-AMP.sh <path/to/dataset> <path/to/checkpoints> <batch/size>

Each of these scripts will by default run 200 warm-up iterations and benchmark the performance during inference in the next 400 iterations.

To have more control, you can run the script by directly providing all relevant run parameters. For example:

python main.py --exec_mode predict --benchmark --data_dir <path/to/dataset> --model_dir <optional, path/to/checkpoint> --batch_size <batch/size> --warmup_steps <warm-up/steps> --max_steps <max/steps>

At the end of the script, a line reporting the best inference throughput will be printed.

Results

The following sections provide details on how we achieved our performance and accuracy in training and inference.

Training accuracy results

Training accuracy: NVIDIA DGX-1 (8x V100 16G)

The following table lists the average DICE score across 5-fold cross-validation. Our results were obtained by running the examples/unet_TRAIN_{FP32, TF-AMP}_{1, 8}GPU.sh training script in the tensorflow:20.02-tf1-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs.

GPUs	Batch size / GPU	Accuracy - FP32	Accuracy - mixed precision	Time to train - FP32 [hours]	Time to train - mixed precision [hours]	Time to train speedup (FP32 to mixed precision)
1	8	0.8884	0.8906	7.08	2.54	2.79
8	8	0.8962	0.8972	0.97	0.37	2.64

To reproduce this result, start the Docker container interactively and run one of the TRAIN scripts:

bash examples/unet_TRAIN_{FP32, TF-AMP}_{1, 8}GPU.sh <path/to/dataset> <path/to/checkpoint> <batch/size>

for example

bash examples/unet_TRAIN_TF-AMP_8GPU.sh /data /results 8

This command will launch a script which will run 5-fold cross-validation training for 40,000 iterations and print the validation DICE score and cross-entropy loss. The time reported is for one fold, which means that the training for 5 folds will take 5 times longer. The default batch size is 8, however if you have less than 16 Gb memory card and you encounter GPU memory issue you should decrease the batch size. The logs of the runs can be found in /results directory once the script is finished.

Training performance results

Training performance: NVIDIA DGX-1 (8x V100 16G)

Our results were obtained by running the examples/unet_TRAIN_BENCHMARK_{TF-AMP, FP32}_{1, 8}GPU.sh training script in the tensorflow:20.02-tf1-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. Performance numbers (in items/images per second) were averaged over 1000 iterations, excluding the first 200 warm-up steps.

GPUs	Batch size / GPU	Throughput - FP32 [img/s]	Throughput - mixed precision [img/s]	Throughput speedup (FP32 - mixed precision)	Weak scaling - FP32	Weak scaling - mixed precision
1	8	18.57	52.27	2.81	N/A	N/A
8	8	138.50	366.88	2.65	7.02	7.46

To achieve these same results, follow the steps in the Training performance benchmark section.

Throughput is reported in images per second. Latency is reported in milliseconds per image.

Inference performance: NVIDIA DGX-1 (1x V100 16G)

Our results were obtained by running the examples/unet_INFER_BENCHMARK_{TF-AMP, FP32}.sh inferencing benchmarking script in the tensorflow:20.02-tf1-py3 NGC container on NVIDIA DGX-1 with (1x V100 16G) GPU.

FP16

Batch size	Resolution	Throughput Avg [img/s]	Latency Avg [ms]	Latency 90% [ms]	Latency 95% [ms]	Latency 99% [ms]
1	572x572x1	133.21	7.507	7.515	7.517	7.519
2	572x572x1	153.45	13.033	13.046	13.048	13.052
4	572x572x1	173.67	23.032	23.054	23.058	23.066
8	572x572x1	181.62	44.047	49.051	49.067	50.880
16	572x572x1	184.21	89.377	94.116	95.024	96.798

FP32

Batch size	Resolution	Throughput Avg [img/s]	Latency Avg [ms]	Latency 90% [ms]	Latency 95% [ms]	Latency 99% [ms]
1	572x572x1	49.97	20.018	20.044	20.048	20.058
2	572x572x1	54.30	36.837	36.865	36.871	36.881
4	572x572x1	56.27	71.085	71.150	71.163	71.187
8	572x572x1	58.41	143.347	154.845	157.047	161.353
16	572x572x1	74.57	222.532	237.184	239.990	245.477

To achieve these same results, follow the steps in the Inference performance benchmark section.

Throughput is reported in images per second. Latency is reported in milliseconds per batch.

Release notes

Changelog

February 2020

• Updated README template
• Added cross-validation for accuracy measurements
• Changed optimizer to Adam and updated accuracy table
• Updated performance values

July 2019

• Added inference benchmark for T4
• Added inference example scripts
• Added inference benchmark measuring latency
• Added TRT/TF-TRT support
• Updated Pre-trained model on NGC registry

June 2019

• Updated README template

April 2019

• Initial release

Known issues

There are no known issues in this release.