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

The SSD300 v1.1 model is based on the\nSSD: Single Shot MultiBox Detector paper, which\ndescribes SSD as \"a method for detecting objects in images using a single deep neural network\".\nThe input size is fixed to 300x300.

The main difference between this model and the one described in the paper is in the backbone.\nSpecifically, the VGG model is obsolete and is replaced by the ResNet-50 model.

From the\nSpeed/accuracy trade-offs for modern convolutional object detectors\npaper, the following enhancements were made to the backbone:

The conv5_x, avgpool, fc and softmax layers were removed from the original classification model.
All strides in conv4_x are set to 1x1.

Detector heads are similar to the ones referenced in the paper, however,\nthey are enhanced by additional BatchNorm layers after each convolution.

Additionally, we removed weight decay on every bias parameter and\nall the BatchNorm layer parameters as described in the\nHighly Scalable Deep Learning Training System with Mixed-Precision:\nTraining ImageNet in Four Minutes paper.

Training of SSD requires computational costly augmentations.\nTo fully utilize GPUs during training we are using the\nNVIDIA DALI library\nto accelerate data preparation pipelines.

This model is trained with mixed precision using Tensor Cores on Volta, Turing,\nand the NVIDIA Ampere GPU architectures. Therefore, researchers can get results\n2x faster than training without Tensor Cores, while experiencing the benefits of\nmixed precision training. This model is tested against each NGC monthly\ncontainer release to ensure consistent accuracy and performance over time.

Model architecture

Despite the changes described in the previous section,\nthe overall architecture, as described in the following diagram, has not changed.

\n \n
\nFigure 1. The architecture of a Single Shot MultiBox Detector model. Image has been taken from the Single Shot MultiBox Detector paper.\n

The backbone is followed by 5 additional convolutional layers.\nIn addition to the convolutional layers, we attached 6 detection heads:

The first detection head is attached to the last conv4_x layer.
The other five detection heads are attached to the corresponding 5 additional layers.

Default configuration

We trained the model for 65 epochs with the following setup:

SGD with momentum (0.9)
Learning rate = 2.6e-3 * number of GPUs * (batch_size / 32)
Learning rate decay – multiply by 0.1 before 43 and 54 epochs
We use linear warmup of the learning rate during the first epoch.

For more information, see the\nAccurate, Large Minibatch SGD: Training ImageNet in 1 Hour paper.

To enable warmup provide argument the --warmup 300

Weight decay:\n* 0 for BatchNorms and biases\n* 5e-4 for other layers

Note: The learning rate is automatically scaled (in other words, multiplied\nby the number of GPUs and multiplied by the batch size divided by 32).

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	SSD300 v1.1 PyTorch
AMP	Yes
APEX DDP	Yes
NVIDIA DALI	Yes

Features

AMP is an abbreviation used for automatic mixed precision training.

DDP stands for DistributedDataParallel and is used for multi-GPU training.

NVIDIA DALI - DALI is a library accelerating data preparation pipeline.\nTo accelerate your input pipeline, you only need to define your data loader\nwith the DALI library.\nFor details, see example sources in this repo or see\nthe DALI documentation

Mixed precision training

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

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

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

For information about:

How to train using mixed precision, see the Mixed Precision Training\npaper and Training With Mixed Precision\ndocumentation.
Techniques used for mixed precision training, see the Mixed-Precision\nTraining of Deep Neural Networks\nblog.
PyTorch AMP, see the PyTorch Automatic Mixed Precision package.

Enabling mixed precision

Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP)\nautocast torch.cuda.amp.autocast which casts variables\nto half-precision upon retrieval, while storing variables in single-precision format.\nFurthermore, to preserve small gradient magnitudes in backpropagation,\na gradient scaling\nstep must be included.

For an in-depth walk through on AMP, check out sample usage\nhere.

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.

Glossary

backbone\n: a part of a many object detection architectures, usually pre-trained for a different,\nsimpler task, like classification.

input pipeline\n: set of operations performed for every item in input data before feeding the neural\nnetwork. Especially for object detection task, the input pipeline can be complex\nand computationally significant. For that reason, solutions like NVIDIA DALI emerged.

object detection\n: a subset of Computer Vision problem. The task of object detection is to localize\npossibly multiple objects on the image and classify them. The difference between\nObject Detection, Image Classification, and Localization are clearly explained in the\nvideo published as a part of the C4W3L01 course.

SSD (Single Shot MultiBox Detector)\n: a name for the detection model described in a paper authored by Liu at al.

ResNet (ResNet-50)\n: a name for the classification model described in a paper authored by He et al.\nIn this repo, it is used as a backbone for SSD.

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Aside from these dependencies,\nensure you have the following software:\n* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n* [PyTorch 22.10 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch)\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\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 PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)\n\nFor those unable to use the [PyTorch 22.10 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch),\nto set up the required environment or create your own container,\nsee the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32,\nperform the following steps using the default parameters of the SSD v1.1 model\non the [COCO 2017](http://cocodataset.org/#download) dataset.\nFor the specifics concerning training and inference,\nsee the [Advanced](#advanced) section.\n\n1. Clone the repository.\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/Detection/SSD\n```\n\n2. Download and preprocess the dataset.\n\nExtract the COCO 2017 dataset with `download_dataset.sh $COCO_DIR`.\nData will be downloaded to the `$COCO_DIR` directory (on the host).\n\n3. Build the SSD300 v1.1 PyTorch NGC container.\n\n```\ndocker build . -t nvidia_ssd\n```\n\n4. Start an interactive session in the NGC container to run training/inference.\n```\ndocker run --rm -it --gpus=all --ipc=host -v $COCO_DIR:/coco nvidia_ssd\n```\n\n**Note**: the default mount point in the container is `/coco`.\n\n5. Start training.\n\nThe `./examples` directory provides several sample scripts for various GPU settings\nand act as wrappers around the `main.py` script.\nThe example scripts need two arguments:\n- A path to the root SSD directory.\n- A path to the COCO 2017 dataset.\n\nRemaining arguments are passed to the `main.py` script.\n\nThe `--save save_dir` flag, saves the model after each epoch in `save_dir` directory.\nThe checkpoints are stored as `/epoch_*.pt`.\n\nUse `python main.py -h` to obtain the list of available options in the `main.py` script.\nFor example, if you want to run 8 GPU training with Tensor Core acceleration and\nsave checkpoints after each epoch, run:\n\n```\nbash ./examples/SSD300_FP16_8GPU.sh . /coco --save $SSD_CHECKPINT_PATH\n```\n\n6. Start validation/evaluation.\n\nThe `main.py` training script automatically runs validation during training.\nThe results from the validation are printed to `stdout`.\n\nTo evaluate a checkpointed model saved in the previous point, run:\n\n```\npython ./main.py --backbone resnet50 --mode evaluation --checkpoint ./models/epoch_*.pt --data /coco\n```\n\n7. Optionally, resume training from a checkpointed model.\n\n```\npython ./main.py --backbone resnet50 --checkpoint ./models/epoch_*.pt --data /coco\n```\n\n8. Start inference/predictions.\n\nYou can check your trained model with a Jupyter notebook provided in the examples directory.\nStart with running a Docker container with a Jupyter notebook server:\n```\ndocker run --rm -it --gpus=all --ipc=host -v $SSD_CHECKPOINT_PATH:/checkpoints/SSD300v1.1.pt -v $COCO_PATH:/datasets/coco2017 -p 8888:8888 nvidia_ssd jupyter-notebook --ip 0.0.0.0 --allow-root\n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset,\nrunning training and inference, and the training results.\n\n### Scripts and sample code\n\nIn the root directory, the most important files are:\n - `main.py`: the script that controls the logic of training and validation of the SSD300 v1.1 model;\n - `Dockerfile`: Instructions for docker to build a container with the basic set of dependencies to run SSD300 v1.1;\n - `requirements.txt`: a set of extra Python requirements for running SSD300 v1.1;\n - `download_dataset.py`: automatically downloads the COCO dataset for training.\n\nThe `ssd/` directory contains modules used to train and evaluate the SSD300 v1.1 model\n - `model.py`: the definition of SSD300 v1.1 model\n - `data.py`: definition of input pipelines used in training and evaluation\n - `train.py`: functions used to train the SSD300 v1.1 model\n - `evaluate.py`: functions used to evaluate the SSD300 v1.1 model\n - `coco_pipeline.py`: definition of input pipeline using NVIDIA DALI\n - `coco.py`: code specific for the COCO dataset\n - `logger.py`: utilities for logging\n - `utils.py`: extra utility functions\n\nThe `examples/` directory contains scripts wrapping common scenarios.\n\n### Parameters\n\n#### The script `main.py`\nThe script for training end evaluating the SSD300 v1.1 model have a variety\nof parameters that control these processes.\n\n##### Common parameters\n`--data`\n: use it to specify, where your dataset is. By default, the script will look for it\nunder the `/coco` directory.\n\n`--checkpoint`\n: allows you to specify the path to the pre-trained model.\n\n`--save`\n: when the flag is turned on, the script will save the trained model checkpoints in the specified directory\n\n`--seed`\n: Use it to specify the seed for RNGs.\n\n`--amp`\n: when the flag is turned on, the AMP features will be enabled.\n\n##### Training related\n\n`--epochs`\n: a number of times the model will see every example from the training dataset.\n\n`--evaluation`\n: after this parameter, list the number of epochs after which evaluation should\nbe performed.\n\n`--learning-rate`\n: initial learning rate.\n\n`--multistep`\n: after this parameter, list the epochs after which learning rate should be decayed.\n\n`--warmup`\n: allows you to specify the number of iterations for which a linear learning-rate\nwarmup will be performed.\n\n`--momentum`\n: momentum argument for SGD optimizer.\n\n`--weight-decay`\n: weight decay argument for SGD optimizer.\n\n`--batch-size`\n: a number of inputs processed at once for each iteration.\n\n`--backbone-path`\n: the path to the checkpointed backbone. When it is not provided, a pre-trained model from torchvision\nwill be downloaded.\n\n##### Evaluation related\n\n`--eval-batch-size`\n: a number of inputs processed at once for each iteration.\n\n##### Utility parameters\n`--help`\n: displays a short description of all parameters accepted by the script.\n\n### Command-line options\n\nAll these parameters can be controlled by passing command-line arguments\nto the `main.py` script. To get a complete list of all command-line arguments\nwith descriptions and default values you can run:\n\n```\npython main.py --help\n```\n\n### Getting the data\n\nThe SSD model was trained on the COCO 2017 dataset. The [val2017](http://cocodataset.org/#download) validation set\nwas used as a validation dataset. PyTorch can work directly on JPEGs,\ntherefore, preprocessing/augmentation is not needed.\n\nThis repository contains the `download_dataset.sh` download script which will automatically\ndownload and preprocess the training, validation and test datasets. By default,\ndata will be downloaded to the `/coco` directory.\n\n#### Dataset guidelines\n\nOur model expects input data aligned in a way a COCO dataset is aligned by the `download_dataset.sh` script.\n`train2017` and `val2017` directories should contain images in JPEG format.\nAnnotation format is described in [the COCO documentation](http://cocodataset.org/#format-data).\n\nThe preprocessing of the data is defined in the `ssd/coco_pipeline.py` module.\n\n##### Data preprocessing\n\nBefore we feed data to the model, both during training and inference, we perform:\n* JPEG decoding\n* normalization with a mean =` [0.485, 0.456, 0.406]` and std dev = `[0.229, 0.224, 0.225]`\n* encoding bounding boxes\n* resizing to 300x300\n\nAdditionally, during training, data is:\n* randomly shuffled\n* samples without annotations are skipped\n\n##### Data augmentation\n\nDuring training we perform the following augmentation techniques:\n* Random crop using the algorithm described in the [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper\n* Random horizontal flip\n* Color jitter\n\n### Training process\n\nTraining the SSD model is implemented in the `main.py` script.\n\nBy default, training is running for 65 epochs. Because evaluation is relatively time consuming,\nit is not running every epoch. With default settings, evaluation is executed after epochs:\n21, 31, 37, 42, 48, 53, 59, 64. The model is evaluated using pycocotools distributed with\nthe COCO dataset.\n Which epochs should be evaluated can be reconfigured with the `--evaluation` argument.\n\nTo run training with Tensor Cores, use the `--amp` flag when running the `main.py` script.\nThe flag `--save ./models` flag enables storing checkpoints after each epoch under `./models/epoch_*.pt`.\n\n### Evaluation process\n\nPycocotools' open-sourced scripts provides a consistent way\nto evaluate models on the COCO dataset. We are using these scripts\nduring validation to measure a 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 ] = 0.27205\n Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.45869\n Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.27884\n Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.08275\n Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.29840\n Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.42722\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.25092\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.36528\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.38262\n Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.13577\n Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.42287\n Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.57277\n```\nThe metric reported in our results is present in the first row.\n\n### Inference process\n\nOur scripts for SSD300 v1.1 presents two ways to run inference.\nTo get meaningful results, you need a pre-trained model checkpoint.\n\nOne way is to run an interactive session on Jupyter notebook, as described in a 8th step of the [Quick Start Guide](#quick-start-guide).\n\nThe container prints Jupyter notebook logs like this:\n```\n[I 16:17:58.935 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret\n[I 16:17:59.769 NotebookApp] JupyterLab extension loaded from /opt/conda/lib/python3.6/site-packages/jupyterlab\n[I 16:17:59.769 NotebookApp] JupyterLab application directory is /opt/conda/share/jupyter/lab\n[I 16:17:59.770 NotebookApp] Serving notebooks from local directory: /workspace\n[I 16:17:59.770 NotebookApp] The Jupyter Notebook is running at:\n[I 16:17:59.770 NotebookApp] http://(65935d756c71 or 127.0.0.1):8888/?token=04c78049c67f45a4d759c8f6ddd0b2c28ac4eab60d81be4e\n[I 16:17:59.770 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[W 16:17:59.774 NotebookApp] No web browser found: could not locate runnable browser.\n[C 16:17:59.774 NotebookApp]\n\n To access the notebook, open this file in a browser:\n file:///root/.local/share/jupyter/runtime/nbserver-1-open.html\n Or copy and paste one of these URLs:\n http://(65935d756c71 or 127.0.0.1):8888/?token=04c78049c67f45a4d759c8f6ddd0b2c28ac4eab60d81be4e\n```\n\nUse the token printed in the last line to start your notebook session.\nThe notebook is in `examples/inference.ipynb`, for example:\n\nhttp://127.0.0.1:8888/notebooks/examples/inference.ipynb?token=04c78049c67f45a4d759c8f6ddd0b2c28ac4eab60d81be4e\n\nAnother way is to run a script `examples/SSD300_inference.py`. It contains the logic from the notebook, wrapped into a Python script. The script contains sample usage.\n\nTo use the inference example script in your own code, you can call the `main` function, providing input image URIs as an argument. The result will be a list of detections for each input image.","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 section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nThe training benchmark was run in various scenarios on A100 80GB and V100 16G GPUs. The benchmark does not require a checkpoint from a fully trained model.\n\nTo benchmark training, run:\n```\ntorchrun --nproc_per_node={NGPU} \\\n main.py --batch-size {bs} \\\n --mode benchmark-training \\\n --benchmark-warmup 100 \\\n --benchmark-iterations 200 \\\n {AMP} \\\n --data {data}\n```\n\nWhere the `{NGPU}` selects number of GPUs used in benchmark, the `{bs}` is the desired\nbatch size, the `{AMP}` is set to `--amp` if you want to benchmark training with\nTensor Cores, and the `{data}` is the location of the COCO 2017 dataset.\n\n`--benchmark-warmup` is specified to omit the first iteration of the first epoch.\n`--benchmark-iterations` is a number of iterations used to measure performance.\n\n#### Inference performance benchmark\n\nInference benchmark was run on 1x A100 80GB GPU and 1x V100 16G GPU. To benchmark inference, run:\n```\npython main.py --eval-batch-size {bs} \\\n --mode benchmark-inference \\\n --benchmark-warmup 100 \\\n --benchmark-iterations 200 \\\n {AMP} \\\n --data {data}\n```\n\nWhere the `{bs}` is the desired batch size, the `{AMP}` is set to `--amp` if you want to benchmark inference with Tensor Cores, and the `{data}` is the location of the COCO 2017 dataset.\n\n`--benchmark-warmup` is specified to omit the first iterations of the first epoch. `--benchmark-iterations` is a number of iterations used to measure performance.\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\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `./examples/SSD300_A100_{FP16,TF32}_{1,4,8}GPU.sh`\nscript in the `pytorch-22.10-py3` NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.\n\n|GPUs |Batch size / GPU|Accuracy - TF32|Accuracy - mixed precision|Time to train - TF32|Time to train - mixed precision|Time to train speedup (TF32 to mixed precision)|\n|-----------|----------------|---------------|---------------------------|--------------------|--------------------------------|------------------------------------------------|\n|1 |64 |0.271 |0.272 |03:19:59 |03:18:35 |100% |\n|4 |64 |0.270 |0.270 |00:51:22 |00:51:31 | 99% |\n|8 |64 |0.270 |0.269 |00:26:10 |00:26:10 | 99% |\n|1 |128 |0.274 |0.271 |03:03:56 |03:03:50 |100% |\n|4 |128 |0.272 |0.270 |00:46:51 |00:47:01 | 99% |\n|8 |128 |0.267 |0.267 |00:23:44 |00:23:46 | 99% |\n|1 |256 |0.272 |0.272 |02:56:37 |02:56:44 | 99% |\n|4 |256 |0.271 |0.267 |00:45:05 |00:45:07 | 99% |\n|8 |256 |0.260 |0.258 |00:22:49 |00:22:56 |100% |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `./examples/SSD300_FP{16,32}_{1,4,8}GPU.sh`\nscript in the `pytorch-22.10-py3` NGC container on NVIDIA DGX-1 with 8x\nV100 16GB GPUs.\n\n|GPUs |Batch size / GPU|Accuracy - FP32|Accuracy - mixed precision|Time to train - FP32|Time to train - mixed precision|Time to train speedup (FP32 to mixed precision)|\n|-----------|----------------|---------------|---------------------------|--------------------|--------------------------------|------------------------------------------------|\n|1 |32 |0.269 |0.271 |20:04:48 |07:25:27 |270% |\n|4 |32 |0.270 |0.269 |05:08:56 |01:58:41 |260% |\n|8 |32 |0.271 |0.269 |02:35:00 |01:00:27 |256% |\n|1 |64 | |0.272 | |06:47:58 | |\n|4 |64 | |0.270 | |01:46:34 | |\n|8 |64 | |0.269 | |00:53:52 | |\n\nDue to smaller size, mixed precision models can be trained with bigger batches. In such cases mixed precision speedup is calculated versus FP32 training with maximum batch size for that precision\n\n##### Training loss plot\n\nHere are example graphs of FP32, TF32 and AMP training on 8 GPU configuration:\n\n![TrainingLoss](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Detection/SSD//img/training_loss.png)\n\n\n##### Training stability test\n\nThe SSD300 v1.1 model was trained for 65 epochs, starting\nfrom 15 different initial random seeds. The training was performed in the `pytorch-22.10-py3` NGC container on\nNVIDIA DGX A100 8x A100 80GB GPUs with batch size per GPU = 128.\nAfter training, the models were evaluated on the test dataset. The following\ntable summarizes the final mAP on the test set.\n\n|**Precision**|**Average mAP**|**Standard deviation**|**Minimum**|**Maximum**|**Median**|\n|------------:|--------------:|---------------------:|----------:|----------:|---------:|\n| AMP | 0.2679503039 | 0.001360494012 | 0.26201 | 0.27013 | 0.26529 |\n| TF32 | 0.2670691823 | 0.001639394102 | 0.26181 | 0.27274 | 0.26492 |\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `main.py` script with the `--mode\nbenchmark-training` flag in the `pytorch-22.10-py3` NGC container on NVIDIA\nDGX A100 (8x A100 80GB) GPUs. Performance numbers (in items/images per second)\nwere averaged over an entire training epoch.\n\n|GPUs |Batch size / GPU|Throughput - TF32|Throughput - mixed precision|Throughput speedup (TF32 - mixed precision)|Weak scaling - TF32 |Weak scaling - mixed precision |\n|-----------|----------------|-----------------|-----------------------------|-------------------------------------------|--------------------------------|------------------------------------------------|\n|1 |64 | 364.27 | 662.91 |181% |100% |100% |\n|4 |64 |1432.73 |2581.24 |180% |393% |389% |\n|8 |64 |2838.76 |5252.84 |185% |779% |792% |\n|1 |128 | 377.18 | 724.41 |192% |100% |100% |\n|4 |128 |1493.13 |2885.55 |193% |395% |398% |\n|8 |128 |2967.23 |5733.98 |193% |786% |791% |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `main.py` script with the `--mode\nbenchmark-training` flag in the `pytorch-22.10-py3` NGC container on NVIDIA\nDGX-1 with 8x V100 16GB GPUs. Performance numbers (in items/images per second)\nwere averaged over an entire training epoch.\n\n|GPUs |Batch size / GPU|Throughput - FP32|Throughput - mixed precision|Throughput speedup (FP32 - mixed precision)|Weak scaling - FP32 |Weak scaling - mixed precision |\n|-----------|----------------|-----------------|-----------------------------|-------------------------------------------|--------------------------------|------------------------------------------------|\n|1 |32 |107.22 | 296.80 |276% |100% |100% |\n|4 |32 |419.54 |1115.59 |265% |391% |375% |\n|8 |32 |840.35 |2153.96 |256% |783% |725% |\n|1 |64 | | 322.81 | | |100% |\n|4 |64 | |1238.27 | | |383% |\n|8 |64 | |2520.50 | | |780% |\n\nDue to smaller size, mixed precision models can be trained with bigger batches. In such cases mixed precision speedup is calculated versus FP32 training with maximum batch size for that precision\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: NVIDIA DGX A100 (1x A100 80GB)\n\nOur results were obtained by running the `main.py` script with `--mode\nbenchmark-inference` flag in the pytorch-22.10-py3 NGC container on NVIDIA\nDGX A100 (1x A100 80GB) GPU.\n\n|Batch size |Throughput - TF32|Throughput - mixed precision|Throughput speedup (TF32 - mixed precision)|Weak scaling - TF32 |Weak scaling - mixed precision |\n|-----------|-----------------|-----------------------------|-------------------------------------------|--------------------|--------------------------------|\n|1 |158.83 | 142.67 | 89% |100% |100% |\n|2 |308.31 | 261.21 | 84% |194% |183% |\n|4 |481.69 | 454.95 | 94% |303% |318% |\n|8 |597.72 | 742.05 |124% |376% |520% |\n|16 |590.44 | 887.01 |150% |371% |621% |\n|32 |708.97 | 970.27 |136% |446% |680% |\n|64 |798.16 |1057.51 |132% |502% |741% |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nOur results were obtained by running the `main.py` script with `--mode\nbenchmark-inference` flag in the pytorch-22.10-py3 NGC container on NVIDIA\nDGX-1 with (1x V100 16GB) GPU.\n\n|Batch size |Throughput - FP32|Throughput - mixed precision|Throughput speedup (FP32 - mixed precision)|Weak scaling - FP32 |Weak scaling - mixed precision |\n|-----------|-----------------|-----------------------------|-------------------------------------------|--------------------|--------------------------------|\n|1 | 93.21 | 84.59 | 90% |100% |100% |\n|2 |148.61 |165.30 |111% |159% |195% |\n|4 |206.82 |304.77 |147% |221% |360% |\n|8 |242.55 |447.25 |184% |260% |528% |\n|16 |292.44 |541.05 |185% |313% |639% |\n|32 |311.61 |605.30 |194% |334% |715% |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.","hasPerformance","policies",[],{"_75":21,"_77":201,"_169":-5,"_175":31,"_177":31,"_193":200,"_180":85},{"_75":23,"_77":23,"_169":-5,"_175":31,"_177":31,"_193":200,"_180":85},{"_75":198,"_77":198,"_169":-5,"_175":31,"_177":31,"_193":199,"_180":85},"21.05.0",[],[],"Latest (22.10.0)","nvidia/dle/ssd_pyt","key","nvidia/dle/resources/ssd_pyt","resourceType","resources","resourceId","ssd_pyt","shortDescription","With a ResNet-50 backbone and a number of architectural modifications, this version provides better accuracy and performance.","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/ssd-pyt)\n\nThe SSD300 v1.1 model is based on the\n[SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which\ndescribes SSD as \"a method for detecting objects in images using a single deep neural network\".\nThe input size is fixed to 300x300.\n\nThe main difference between this model and the one described in the paper is in the backbone.\nSpecifically, the VGG model is obsolete and is replaced by the ResNet-50 model.\n\nFrom the\n[Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/abs/1611.10012)\npaper, the following enhancements were made to the backbone:\n* The conv5_x, avgpool, fc and softmax layers were removed from the original classification model.\n* All strides in conv4_x are set to 1x1.\n\nDetector heads are similar to the ones referenced in the paper, however,\nthey are enhanced by additional BatchNorm layers after each convolution.\n\nAdditionally, we removed weight decay on every bias parameter and\nall the BatchNorm layer parameters as described in the\n[Highly Scalable Deep Learning Training System with Mixed-Precision:\nTraining ImageNet in Four Minutes](https://arxiv.org/abs/1807.11205) paper.\n\nTraining of SSD requires computational costly augmentations.\nTo fully utilize GPUs during training we are using the\n[NVIDIA DALI](https://github.com/NVIDIA/DALI) library\nto accelerate data preparation pipelines.\n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing,\nand the NVIDIA Ampere GPU architectures. Therefore, researchers can get results\n2x faster than training without Tensor Cores, while experiencing the benefits of\nmixed precision training. This model is tested against each NGC monthly\ncontainer release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nDespite the changes described in the previous section,\nthe overall architecture, as described in the following diagram, has not changed.\n\n

\n \n
\nFigure 1. The architecture of a Single Shot MultiBox Detector model. Image has been taken from the Single Shot MultiBox Detector paper.\n

\n\nThe backbone is followed by 5 additional convolutional layers.\nIn addition to the convolutional layers, we attached 6 detection heads:\n* The first detection head is attached to the last conv4_x layer.\n* The other five detection heads are attached to the corresponding 5 additional layers.\n\n### Default configuration\nWe trained the model for 65 epochs with the following setup:\n* SGD with momentum (0.9)\n* Learning rate = 2.6e-3 * number of GPUs * (batch_size / 32)\n* Learning rate decay – multiply by 0.1 before 43 and 54 epochs\n* We use linear warmup of the learning rate during the first epoch.\n\nFor more information, see the\n [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677) paper.\n\nTo enable warmup provide argument the `--warmup 300`\n* Weight decay:\n * 0 for BatchNorms and biases\n * 5e-4 for other layers\n\n**Note**: The learning rate is automatically scaled (in other words, multiplied\nby the number of GPUs and multiplied by the batch size divided by 32).\n\n### Feature support matrix\n\nThe following features are supported by this model.\n\n| **Feature** | **SSD300 v1.1 PyTorch** |\n|:---------:|:----------:|\n|[AMP](https://pytorch.org/docs/stable/amp.html) | Yes |\n|[APEX DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) | Yes |\n|[NVIDIA DALI](https://docs.nvidia.com/deeplearning/sdk/dali-release-notes/index.html) | Yes |\n\n#### Features\n\n[AMP](https://pytorch.org/docs/stable/amp.html) is an abbreviation used for automatic mixed precision training.\n\n[DDP](https://nvidia.github.io/apex/parallel.html) stands for DistributedDataParallel and is used for multi-GPU training.\n\n[NVIDIA DALI](https://docs.nvidia.com/deeplearning/sdk/dali-release-notes/index.html) - DALI is a library accelerating data preparation pipeline.\nTo accelerate your input pipeline, you only need to define your data loader\nwith the DALI library.\nFor details, see example sources in this repo or see\nthe [DALI documentation](https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/index.html)\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in\na computational method. [Mixed precision](https://arxiv.org/abs/1710.03740)\ntraining offers significant computational speedup by performing operations\nin half-precision format, while storing minimal information in single-precision\nto retain as much information as possible in critical parts of the network.\nSince the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores)\nin Volta, and following with both the Turing and Ampere architectures, significant training speedups are\nexperienced by switching to mixed precision -- up to 3x overall speedup\non the most arithmetically intense model architectures. Using mixed precision\ntraining requires two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\n\nThe ability to train deep learning networks with lower precision was introduced\nin the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/)\nin the NVIDIA Deep Learning SDK.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740)\npaper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)\ndocumentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision\nTraining of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)\nblog.\n- PyTorch AMP, see the [PyTorch Automatic Mixed Precision package](https://pytorch.org/docs/stable/amp.html).\n\n#### Enabling mixed precision\n\nMixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP)\nautocast [torch.cuda.amp.autocast](https://pytorch.org/docs/stable/amp.html#autocasting) which casts variables\nto half-precision upon retrieval, while storing variables in single-precision format.\nFurthermore, to preserve small gradient magnitudes in backpropagation,\na [gradient scaling](https://pytorch.org/docs/stable/amp.html#gradient-scaling)\nstep must be included.\n\nFor an in-depth walk through on AMP, check out sample usage\n[here](https://pytorch.org/docs/stable/amp.html).\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.\n\n### Glossary\n\nbackbone\n: a part of a many object detection architectures, usually pre-trained for a different,\nsimpler task, like classification.\n\ninput pipeline\n: set of operations performed for every item in input data before feeding the neural\nnetwork. Especially for object detection task, the input pipeline can be complex\nand computationally significant. For that reason, solutions like NVIDIA DALI emerged.\n\nobject detection\n: a subset of Computer Vision problem. The task of object detection is to localize\npossibly multiple objects on the image and classify them. The difference between\nObject Detection, Image Classification, and Localization are clearly explained in the\nvideo published as a part of the [C4W3L01 course](https://www.youtube.com/watch?v=GSwYGkTfOKk).\n\nSSD (Single Shot MultiBox Detector)\n: a name for the detection model described in a [paper authored by Liu at al.](https://arxiv.org/abs/1512.02325)\n\nResNet (ResNet-50)\n: a name for the classification model described in a [paper authored by He et al.](https://arxiv.org/abs/1512.03385)\nIn this repo, it is used as a backbone for SSD.","displayName","SSD v1.1 for PyTorch",["D",1675203390937],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/ssd_pyt","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",{"_106":1420,"_108":1421},"ngcApps",[1373],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1354":1355,"_1356":1357,"_1358":1359,"_1360":1361,"_1362":255,"_138":279,"_1363":1364,"_1365":56,"_1366":85,"_1367":85,"_1368":1369,"_1370":85,"_1371":1372},"amplitude",{"_1351":1352,"_952":-7,"_1353":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1345":1346,"_1347":1348,"_1349":1350},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1227":359,"_1228":1229,"_1230":1231,"_1232":1233},"announcements",[],"consentState",{"_317":1217,"_1218":31,"_1219":31,"_1220":85,"_1221":1222},"consentConfig",{"_299":300,"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318},"showContactAdmin",1784221266794,[],[],[293,294,295,296,297],{"_45":59,"_47":70,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":298,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},5,"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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Information collected with these cookies is used to measure and find ways to improve website 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