[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10,"_11":12},"actionData","errors","root",{"_67":248,"_249":250,"_251":252,"_253":254,"_255":256,"_257":26,"_258":259,"_260":261,"_262":26,"_263":26,"_264":26,"_265":26,"_266":267,"_268":-5,"_269":270,"_271":272,"_273":274,"_275":93,"_276":95,"_277":278,"_87":-5,"_279":26,"_280":-5,"_281":282,"_116":283,"_284":285,"_286":287,"_288":289,"_290":291,"_292":26,"_21":22,"_23":293,"_25":26,"_27":294,"_29":295,"_31":296},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/route",{"_102":103,"_17":104,"_105":106,"_107":108,"_109":26,"_110":111,"_112":26,"_113":26,"_114":115,"_116":14,"_117":-5,"_118":119,"_120":-5,"_121":122,"_123":124,"_125":26,"_126":127,"_21":22,"_23":128,"_25":26,"_27":129,"_29":130,"_31":131},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/_access/route",{"_64":65,"_66":-5,"_67":68,"_69":70,"_21":22,"_23":71,"_25":26,"_27":72,"_29":73,"_31":74},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/release-notes/route",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32},"versionId","1","releaseNotes","## Release Notes\n\n1. Mar 18, 2019\n- first release\n- also available in [github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Detection/SSD)","html","

Release Notes

Mar 18, 2019

first release
also available in github

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The SSD320 v1.2 model is based on the SSD: Single Shot MultiBox Detector paper, which describes SSD as \"a method for detecting objects in images using a single deep neural network\".\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.5x 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

Our implementation is based on the existing model from the TensorFlow models repository.\nThe network was altered in order to improve accuracy and increase throughput. Changes include:

Replacing the VGG backbone with the more popular ResNet50.
Adding multi-scale detection to the backbone using Feature Pyramid Networks.
Replacing the original hard negative mining loss function with Focal Loss.
Decreasing the input size to 320 x 320.

Default configuration

We trained the model for 12500 steps (27 epochs) with the following setup:

SGDR with cosine decay learning rate
Learning rate base = 0.16
Momentum = 0.9
Warm-up learning rate = 0.0693312
Warm-up steps = 1000
Batch size per GPU = 32
Number of GPUs = 8

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

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

Feature	Transformer-XL
Automatic mixed precision (AMP)	Yes
Horovod Multi-GPU (NCCL)	Yes

Features

TF-AMP - a\ntool that enables Tensor Core-accelerated training. Refer to the Enabling\nmixed precision section for more details.

Horovod - Horovod\nis a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet.\nThe goal of Horovod is to make distributed deep learning fast and easy to use.\nFor more information about how to get started with Horovod, see the Horovod:\nOfficial repository.

Multi-GPU training with Horovod - our model\nuses Horovod to implement efficient multi-GPU training with NCCL. For details,\nsee example sources in this repository or see the TensorFlow\ntutorial.

Mixed precision training

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

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 enablethe full\nmixed precision methodology\nin your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe 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,\nand the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work\nwith the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment\nvariable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically\nrewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.

For information about:

How to train using mixed precision, see the Mixed Precision\nTraining paper and Training With Mixed\nPrecision\ndocumentation.
Techniques used for mixed precision training, see the Mixed-Precision\nTraining of Deep Neural\nNetworks\nblog.
How to access and enable AMP for TensorFlow, see Using\nTF-AMP\nfrom the TensorFlow User Guide.

Enabling mixed precision

Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP)\nextension which casts variables to half-precision upon retrieval, while storing variables\nin single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation,\na loss scaling\nstep must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by\nusing simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed\nprecision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations.\nFirst, programmers need not modify network model code, reducing development and maintenance effort.\nSecond, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.

To enable mixed precision, you can simply add the values to the environmental variables inside your training script:

Enable TF-AMP graph rewrite:

os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n

Enable Automated Mixed Precision:

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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Changes we have made include:\n- Replacing the VGG backbone with the more popular ResNet50.\n- Adding multi-scale detection to the backbone using [Feature Pyramid Networks](https://arxiv.org/pdf/1612.03144.pdf).\n- Replacing the original hard negative mining loss function with [Focal Loss](https://arxiv.org/pdf/1708.02002.pdf).\n- Decreasing the input size to 320 x 320.\n\nOur implementation is based on the existing [model from the TensorFlow models repository](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config).\n\nThis model trains with mixed precision tensor cores on NVIDIA Volta GPUs, therefore you can get results much faster than training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\nThe following features were implemented in this model:\n- Data-parallel multi-GPU training with Horovod.\n- Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.\n- Tensor Core operations to maximize throughput using NVIDIA Volta GPUs.\n- Dynamic loss scaling for tensor cores (mixed precision) training.\n\nBecause of these enhancements, the SSD320 v1.2 model achieves higher accuracy.\n\n### Default configuration\nWe trained the model for 12500 steps (27 epochs) with the following setup:\n- [SGDR](https://arxiv.org/pdf/1608.03983.pdf) with cosine decay learning rate\n- Learning rate base = 0.16 \n- Momentum = 0.9\n- Warm-up learning rate = 0.0693312\n- Warm-up steps = 1000\n- Batch size per GPU = 32\n- Number of GPUs = 8","UPLOAD_COMPLETE","releaseType","createdDate",["D",1553138395291],"updatedDate","totalSizeInBytes",7591363,"totalFileCount",542,"isSigned","malwareScanStatus","isScanned","hasReleaseNotes","setup","## Setup\n\nThe following section list the requirements in order to start training the SSD320 v1.2 model.\n\n### Requirements\nThis repository contains `Dockerfile` which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following software:\n* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n* [TensorFlow 19.03-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) (or later) NGC container\n* [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the\nfollowing sections from the NVIDIA GPU Cloud Documentation and the Deep Learning\nDocumentation:\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)","hasSetup","quickStartGuide","## Quick Start Guide\nTo train your model using mixed precision with tensor cores or using FP32,\nperform the following steps using the default parameters of the SSD320 v1.2 model on the\n[COCO 2017](http://cocodataset.org/#download) dataset.\n\n\n\n### 1. Clone the repository.\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/TensorFlow/Detection/SSD\n```\n\n### 2. Build the SSD320 v1.2 TensorFlow NGC container.\n\n```\ndocker build . -t nvidia_ssd\n```\n\n### 3. Download and preprocess the dataset. \n\nExtract the COCO 2017 dataset with:\n\n```\ndownload_all.sh nvidia_ssd \n```\n\nData will be downloaded, preprocessed to tfrecords format and saved in the `` directory (on the host).\nMoreover the script will download pre-trained RN50 checkpoint in the `` directory\n\n\n### 4. Launch the NGC container to run training/inference.\n```\nnvidia-docker run --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -v :/data -v :/checkpoints --ipc=host nvidia_ssd\n```\n\n### 5. Start training.\n\nThe `./examples` directory provides several sample scripts for various GPU settings and act as wrappers around\n`object_detection/model_main.py` script. The example scripts can be modified by arguments: \n- A path to directory for checkpoints\n- Additional arguments to `object_detection/model_main.py`\n\nIf you want to run 8 GPUs, training with tensor cores acceleration and save checkpoints in `/checkpoints` directory, run:\n\n```\nbash ./examples/SSD320_FP16_8GPU.sh /checkpoints\n```\n\n\n### 6. Start validation/evaluation.\n\n\nThe `model_main.py` training script automatically runs validation during training.\nThe results from the validation are printed to `stdout`.\n\nPycocotools' open-sourced scripts provides a consistent way to evaluate models on the COCO dataset.\nWe are using these scripts during validation to measure models performance in AP metric.\nMetrics below are evaluated using pycocotools' methodology,\nin the following format:during validation to measure models performance in AP metric.\nMetrics below are evaluated using pycocotools' methodology, in the following format:\n```\n Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.273\n Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.423\n Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.291\n Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.024\n Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.218\n Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.451\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.257\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427\n Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.070\n Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418\n Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.645\n```\nThe metric reported in our results is present in the first row.\n\nTo evaluate a checkpointed model saved in the previous step, you can use script from examples directory.\nIf you want to run inference with tensor cores acceleration, run:\n\n```\nbash examples/SSD320_evaluate.sh \n```\n\n### Details\n\nThe following sections provide greater details of the dataset, running training and inference, and the training results.\n\n#### Command line options\nThe SSD model training is conducted by the script from the object_detection library, `model_main.py`.\nOur experiments were done with settings described in the `examples` directory.\nIf you would like to get more details about available arguments, please run:\n\n```\npython object_detection/model_main.py --help\n```\n\n#### Getting the data\n\nThe SSD320 v1.2 model was trained on the COCO 2017 dataset. The val2017 validation set was used as a validation dataset.\nThe `download_data.sh` script will preprocess the data to tfrecords format.\n\nThis repository contains the `download_dataset.sh` script which will automatically download and preprocess the training,\nvalidation and test datasets. By default, data will be downloaded to the `/data` directory.\n\n#### Training process\nTraining the SSD model is implemented in the `object_detection/model_main.py` script. \n\nAll training parameters are set in the config files. Because evaluation is relatively time consuming,\nit does not run every epoch. By default, evaluation is executed only once at the end of the training.\nThe model is evaluated using pycocotools distributed with the COCO dataset.\nThe number of evaluations can be changed using the `eval_count` parameter.\n\nTo run training with tensor cores, use `./examples/SSD320_FP16_{1,4,8}GPU.sh` scripts. For more details see [Enabling mixed precision](#enabling-mixed-precision) section below.\n\n##### Data preprocessing\nBefore we feed data to the model, both during training and inference, we perform:\n*\tNormalization\n*\tEncoding bounding boxes\n* Resize to 320x320\n\n##### Data augmentation\nDuring training we perform the following augmentation techniques:\n*\tRandom crop\n*\tRandom horizontal flip\n*\tColor jitter\n\n\n\n#### Enabling mixed precision\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format,\nwhile storing minimal information in single-precision to retain as much information as possible in critical parts of the network.\nSince the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures,\nsignificant training speedups are experienced by switching to mixed precision --\nup to 3x overall speedup on the most arithmetically intense model architectures.\nUsing [mixed precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) training previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Manually 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.\nAMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe 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,\nand the loss scaling is automatically applied inside of supported optimizers.\nAMP 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.\nIt accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\n\nFor information about:\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/sdk/mixed-precision-training/index.html) documentation.\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- 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.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Scripts and sample code\n\n* `Dockerfile`: a container with the basic set of dependencies to run SSD\n\nIn the `model/research/object_detection` directory, the most important files are:\n\n* `model_main.py`: serves as the entry point to launch the training and inference\n* `models/ssd_resnet_v1_fpn_feature_extractor.py`: implementation of the model\n* `metrics/coco_tools.py`: implementation of mAP metric\n* `utils/exp_utils.py`: utility functions for running training and benchmarking\n\n### Parameters\n\nThe complete list of available parameters for the `model/research/object_detection/model_main.py` script contains:\n\n```\n./object_detection/model_main.py:\n --[no]allow_xla: Enable XLA compilation\n (default: 'false')\n --checkpoint_dir: Path to directory holding a checkpoint. If `checkpoint_dir` is provided, this binary operates in\n eval-only mode, writing resulting metrics to `model_dir`.\n --eval_count: How many times the evaluation should be run\n (default: '1')\n (an integer)\n --[no]eval_training_data: If training data should be evaluated for this job. Note that one call only use this in eval-\n only mode, and `checkpoint_dir` must be supplied.\n (default: 'false')\n --hparams_overrides: Hyperparameter overrides, represented as a string containing comma-separated hparam_name=value\n pairs.\n --model_dir: Path to output model directory where event and checkpoint files will be written.\n --num_train_steps: Number of train steps.\n (an integer)\n --pipeline_config_path: Path to pipeline config file.\n --raport_file: Path to dlloger json\n (default: 'summary.json')\n --[no]run_once: If running in eval-only mode, whether to run just one round of eval vs running continuously (default).\n (default: 'false')\n --sample_1_of_n_eval_examples: Will sample one of every n eval input examples, where n is provided.\n (default: '1')\n (an integer)\n --sample_1_of_n_eval_on_train_examples: Will sample one of every n train input examples for evaluation, where n is\n provided. This is only used if `eval_training_data` is True.\n (default: '5')\n (an integer)\n```\n\n### Command line options\nThe SSD model training is conducted by the script from the object_detection library, `model_main.py`.\nOur experiments were done with settings described in the `examples` directory.\nIf you would like to get more details about available arguments, please run:\n\n```\npython object_detection/model_main.py --help\n```\n\n### Getting the data\n\nThe SSD320 v1.2 model was trained on the COCO 2017 dataset. The val2017 validation set was used as a validation dataset.\nThe `download_data.sh` script will preprocess the data to tfrecords format.\n\nThis repository contains the `download_dataset.sh` script which will automatically download and preprocess the training,\nvalidation and test datasets. By default, data will be downloaded to the `/data/coco2017_tfrecords` directory.\n\n### Training process\nTraining the SSD model is implemented in the `object_detection/model_main.py` script. \n\nAll training parameters are set in the config files. Because evaluation is relatively time consuming,\nit does not run every epoch. By default, evaluation is executed only once at the end of the training.\nThe model is evaluated using pycocotools distributed with the COCO dataset.\nThe number of evaluations can be changed using the `eval_count` parameter.\n\nTo run training with tensor cores, use `./examples/SSD320_FP16_{1,4,8}GPU.sh` scripts. For more details see [Enabling mixed precision](#enabling-mixed-precision) section below.\n\n#### Data preprocessing\nBefore we feed data to the model, both during training and inference, we perform:\n*\tNormalization\n*\tEncoding bounding boxes\n* Resize to 320x320\n\n#### Data augmentation\nDuring training we perform the following augmentation techniques:\n*\tRandom crop\n*\tRandom horizontal flip\n*\tColor jitter\n\n### Enabling mixed precision\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format,\nwhile storing minimal information in single-precision to retain as much information as possible in critical parts of the network.\nSince the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures,\nsignificant training speedups are experienced by switching to mixed precision --\nup to 3x overall speedup on the most arithmetically intense model architectures.\nUsing [mixed precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) training previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Manually 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.\nAMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe 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,\nand the loss scaling is automatically applied inside of supported optimizers.\nAMP 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.\nIt accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\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/sdk/mixed-precision-training/index.html) documentation.\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- 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.","hasAdvanced","performance","## Performance\n\n### Benchmarking\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\nTraining benchmark was run in various scenarios on V100 16G GPU. For each scenario, batch size was set to 32. \n\nTo benchmark training, run:\n\n```\nbash examples/SSD320_{PREC}_{NGPU}GPU_BENCHMARK.sh\n```\n\n\nWhere the `{NGPU}` defines number of GPUs used in benchmark, and the `{PREC}` defines precision.\nThe benchmark runs training with only 1200 steps and computes average training speed of last 300 steps.\n\n\n#### Inference performance benchmark\nInference benchmark was run with various batch-sizes on V100 16G GPU.\nFor inference we are using single GPU setting. Examples are taken from the validation dataset.\n\nTo benchmark inference, run:\n\n```\nbash examples/SSD320_FP{16,32}_inference.sh --batch_size --checkpoint_dir \n```\n\nBatch size for the inference benchmark is controlled by the `--batch_size` argument,\nwhile the checkpoint is provided to the script with the `--checkpoint_dir` argument.\n\nThe benchmark script provides extra arguments for extra control over the experiment.\nWe were using default values for the extra arguments during the experiments. For more details about them, please run:\n\n```\nbash examples/SSD320_FP16_inference.sh --help\n```\n\n### Results\n\nThe following sections provide details on how we achieved our performance and accuracy in training and inference.\n\n#### Training accuracy results\nOur results were obtained by running the `./examples/SSD320_FP{16,32}_{1,4,8}GPU.sh` script in the TensorFlow-19.03-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.\nAll the results are obtained with batch size set to 32.\n\n| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **FP32 mAP** | **Training time with FP32** |\n|:------------------:|:-----------------------:|:--------------------------------------:|:------------:|:---------------------------:|\n| 1 | 0.276 | 7h 17min | 0.278 | 10h 20min |\n| 4 | 0.277 | 2h 15min | 0.275 | 2h 53min |\n| 8 | 0.269 | 1h 19min | 0.268 | 1h 37min |\n\n\nHere are example graphs of FP32 and FP16 training on 8 GPU configuration:\n\n![TrainingLoss](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/Detection/SSD/img/training_loss.png)\n\n![ValidationAccuracy](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/Detection/SSD/img/validation_accuracy.png)\n\n#### Training performance results\n\nOur results were obtained by running:\n\n```\npython bash examples/SSD320_FP*GPU_BENCHMARK.sh\n```\n\nscripts in the TensorFlow-19.03-py3 NGC container on NVIDIA DGX-1 with V100 16G GPUs. \n\n\n| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s** | **FP32 img/s** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with FP32** |\n|:------------------:|:----------------------:|:-------------------------:|:--------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:|\n| 1 | 32 | 124.97 | 87.87 | 1.42 | 1.00 | 1.00 |\n| 4 | 32 | 430.79 | 330.35 | 1.39 | 3.45 | 3.76 |\n| 8 | 32 | 752.04 | 569.01 | 1.32 | 6.02 | 6.48 |\n\nTo achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.\n\nThose results can be improved when [XLA](https://www.tensorflow.org/xla) is used \nin conjunction with mixed precision, delivering up to 2x speedup over FP32 on a single GPU (~179 img/s).\nHowever XLA is still considered experimental.\n\n#### Inference performance results\n\nOur results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-19.03-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs.\n\n\n| **Batch size** | **Mixed precision img/s** | **FP32 img/s** |\n|:--------------:|:-------------------------:|:--------------:|\n| 1 | 93.37 | 97.29 |\n| 2 | 135.33 | 134.04 |\n| 4 | 171.70 | 163.38 |\n| 8 | 189.25 | 174.47 |\n| 16 | 187.62 | 175.42 |\n| 32 | 187.37 | 175.07 |\n| 64 | 191.40 | 177.75 |\n\nTo achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.","hasPerformance","policies",[],{"_179":190,"_180":191,"_147":-5,"_155":26,"_157":26,"_171":189,"_158":65},{"_179":188,"_180":188,"_147":-5,"_155":26,"_157":26,"_171":189,"_158":65},{"_179":186,"_180":186,"_147":-5,"_155":26,"_157":26,"_171":187,"_158":65},{"_179":184,"_180":184,"_147":-5,"_155":26,"_157":26,"_171":185,"_158":65},{"_179":182,"_180":182,"_147":-5,"_155":26,"_157":26,"_171":183,"_158":65},{"_179":14,"_180":14,"_147":-5,"_155":26,"_157":26,"_171":181,"_158":65},"id","name",[],"2",[],"20.06.0",[],"20.06.1",[],"20.06.2",[],"-","Latest (20.06.2)","nvidia/ssd_for_tensorflow","key","nvidia/resources/ssd_for_tensorflow","resourceId","ssd_for_tensorflow","shortDescription","With a ResNet-50 backbone and a number of architectural modifications, this version provides better accuracy and performance.","The SSD320 v1.2 model is based on the [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which describes SSD as \"a method for detecting objects in images using a single deep neural network\".\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.5x 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.\n### Model architecture\n\nOur implementation is based on the existing [model from the TensorFlow models repository](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config).\nThe network was altered in order to improve accuracy and increase throughput. Changes include:\n- Replacing the VGG backbone with the more popular ResNet50.\n- Adding multi-scale detection to the backbone using [Feature Pyramid Networks](https://arxiv.org/pdf/1612.03144.pdf).\n- Replacing the original hard negative mining loss function with [Focal Loss](https://arxiv.org/pdf/1708.02002.pdf).\n- Decreasing the input size to 320 x 320.\n\n### Default configuration\nWe trained the model for 12500 steps (27 epochs) with the following setup:\n- [SGDR](https://arxiv.org/pdf/1608.03983.pdf) with cosine decay learning rate\n- Learning rate base = 0.16 \n- Momentum = 0.9\n- Warm-up learning rate = 0.0693312\n- Warm-up steps = 1000\n- Batch size per GPU = 32\n- Number of GPUs = 8\n\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| **Feature** | **Transformer-XL** |\n|:------------|-------------------:|\n|[Automatic mixed precision (AMP)](https://nvidia.github.io/apex/amp.html) | Yes |\n|[Horovod Multi-GPU (NCCL)](https://github.com/horovod/horovod) | Yes |\n\n#### Features\n\n[TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) - a \ntool that enables Tensor Core-accelerated training. Refer to the [Enabling\nmixed precision](#enabling-mixed-precision) section for more details.\n\n[Horovod](https://github.com/horovod/horovod) - Horovod \nis a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet.\nThe goal of Horovod is to make distributed deep learning fast and easy to use.\nFor more information about how to get started with Horovod, see the [Horovod:\nOfficial repository](https://github.com/horovod/horovod).\n\n[Multi-GPU training with Horovod](https://github.com/horovod/horovod/#usage) - our model \nuses Horovod to implement efficient multi-GPU training with NCCL. For details,\nsee example sources in this repository or see the [TensorFlow\ntutorial](https://github.com/horovod/horovod/#usage).\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a\ncomputational method.\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant\ncomputational speedup by performing operations in half-precision format while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of [Tensor\nCores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the\nTuring and Ampere architectures, significant training speedups are experienced by switching to\nmixed precision -- up to 3x overall speedup on the most arithmetically intense\nmodel architectures. Using mixed precision training previously required two\nsteps:\n\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 enablethe full\n[mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow)\nin your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe 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,\nand the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work\nwith the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment\nvariable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically\nrewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n\n* How to train using mixed precision, see the [Mixed Precision\n Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed\n Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)\n documentation.\n* Techniques used for mixed precision training, see the [Mixed-Precision\n Training of Deep Neural\n Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)\n blog.\n* How to access and enable AMP for TensorFlow, see [Using\n TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp)\n from the TensorFlow User Guide. \n\n#### Enabling mixed precision\n\nMixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP)\nextension which casts variables to half-precision upon retrieval, while storing variables\nin single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation,\na [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling)\nstep must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by\nusing simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed\nprecision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations.\nFirst, programmers need not modify network model code, reducing development and maintenance effort.\nSecond, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.\n\nTo enable mixed precision, you can simply add the values to the environmental variables inside your training script:\n- Enable TF-AMP graph rewrite:\n ```\n os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n ```\n \n- Enable Automated Mixed Precision:\n ```\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","SSD v1.2 for TensorFlow",["D",1680632747098],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/ssd_for_tensorflow","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",7581582,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[233,234],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_200":244,"_236":245,"_238":246,"_240":241},{"_200":235,"_236":237,"_238":239,"_240":241},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_242":243,"_236":237},"category","label","field","platform","Computer Vision","soln_computer_vision",{"_242":247,"_236":245},"technology",{"_85":1423,"_87":1424},"ngcApps",[1376],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1357":1358,"_1359":1360,"_1361":1362,"_1363":1364,"_1365":259,"_116":283,"_1366":1367,"_1368":50,"_1369":65,"_1370":65,"_1371":1372,"_1373":65,"_1374":1375},"amplitude",{"_1354":1355,"_956":-7,"_1356":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1348":1349,"_1350":1351,"_1352":1353},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1231":363,"_1232":1233,"_1234":1235,"_1236":1237},"announcements",[],"consentState",{"_321":1221,"_1222":26,"_1223":26,"_1224":65,"_1225":1226},"consentConfig",{"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322},"showContactAdmin",1783565897481,[],[],[297,298,299,300,301,302],{"_39":54,"_41":62,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":57,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":56,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":53,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"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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