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

The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model.

The difference between v1 and v1.5 is that, in the bottleneck blocks which requires\ndownsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.

This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a smallperformance drawback (~5% imgs/sec).

The model is initialized as described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 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.

We are currently working on adding NHWC data layout support for Mixed Precision training.

Default configuration

The following sections highlight the default configurations for the ResNet50 model.

Optimizer

This model uses SGD with momentum optimizer with the following hyperparameters:

Momentum (0.875)
Learning rate (LR) = 0.256 for 256 batch size, for other batch sizes we linearly\nscale the learning rate.
Learning rate schedule - we use cosine LR schedule
For bigger batch sizes (512 and up) we use linear warmup of the learning rate\nduring the first couple of epochs\naccording to Training ImageNet in 1 hour.\nWarmup length depends on the total training length.
Weight decay (WD)= 3.0517578125e-05 (1/32768).
We do not apply WD on Batch Norm trainable parameters (gamma/bias)
Label smoothing = 0.1
We train for:\n
- 50 Epochs -> configuration that reaches 75.9% top1 accuracy
- 90 Epochs -> 90 epochs is a standard for ImageNet networks
- 250 Epochs -> best possible accuracy.
\n
For 250 epoch training we also use MixUp regularization.

Data augmentation

This model uses the following data augmentation:

For training:\n
- Normalization
- Random resized crop to 224x224\n
  - Scale from 8% to 100%
  - Aspect ratio from 3/4 to 4/3
  \n
- Random horizontal flip
\n
For inference:\n
- Normalization
- Scale to 256x256
- Center crop to 224x224
\n

Other training recipes

This script does not target any specific benchmark.\nThere are changes that others have made which can speed up convergence and/or increase accuracy.

One of the more popular training recipes is provided by fast.ai.

The fast.ai recipe introduces many changes to the training procedure, one of which is progressive resizing of the training images.

The first part of training uses 128px images, the middle part uses 224px images, and the last part uses 288px images.\nThe final validation is performed on 288px images.

Training script in this repository performs validation on 224px images, just like the original paper described.

These two approaches can't be directly compared, since the fast.ai recipe requires validation on 288px images,\nand this recipe keeps the original assumption that validation is done on 224px images.

Using 288px images means that a lot more FLOPs are needed during inference to reach the same accuracy.

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	ResNet50
DALI	Yes
APEX AMP	Yes

Features

\n
NVIDIA DALI - DALI is a library accelerating data preparation pipeline. To accelerate your input pipeline, you only need to define your data loader\nwith the DALI library. For more information about DALI, refer to the DALI product documentation.
\n
\n
APEX is a PyTorch extension that contains utility libraries, such as Automatic Mixed Precision (AMP), which require minimal network code changes to leverage Tensor Cores performance. Refer to the Enabling mixed precision section for more details.
\n

DALI

We use NVIDIA DALI,\nwhich speeds up data loading when CPU becomes a bottleneck.\nDALI can use CPU or GPU, and outperforms the PyTorch native dataloader.

Run training with --data-backends dali-gpu or --data-backends dali-cpu to enable DALI.\nFor DGXA100 and DGX1 we recommend --data-backends dali-cpu, for DGX2 we recommend --data-backends dali-gpu.

Mixed precision training

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

Enabling mixed precision

Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), a library from APEX that casts variables to half-precision upon retrieval,\nwhile storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients.\nIn PyTorch, loss scaling can be easily applied by using scale_loss() method provided by AMP. The scaling value to be used can be dynamic or fixed.

For an in-depth walk through on AMP, check out sample usage here. APEX is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance.

To enable mixed precision, you can:

\n
Import AMP from APEX:
\n
```
from apex import amp\n
```
\n

Wrap model and optimizer in amp.initialize:

model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\", loss_scale=\"dynamic\")\n

Scale loss before backpropagation:

with amp.scale_loss(loss, optimizer) as scaled_loss:\n  scaled_loss.backward()\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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September 2018\n * Initial release\n2. January 2019\n * Added options Label Smoothing, fan-in initialization, skipping weight decay on batch norm gamma and bias.\n3. May 2019\n * Cosine LR schedule\n * MixUp regularization\n * DALI support\n * DGX2 configurations\n * gradients accumulation\n4. July 2019\n * DALI-CPU dataloader\n * Updated README\n5. July 2020\n * Added A100 scripts\n * Updated README\n6. February 2021\n * Moved from APEX AMP to Native AMP\n\n### Known issues\n\nThere are no known issues with this model.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training the ResNet50 model.\n\n### Requirements\n\nThis repository contains Dockerfile which extends the PyTorch NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n\n* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n* [PyTorch 21.03-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) or newer\n* Supported GPUs:\n * [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n * [NVIDIA Turing architecture](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\nDGX Documentation:\n* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)\n* [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)\n\nFor those unable to use the PyTorch NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","### 1. Clone the repository.\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/Classification/\n```\n\n### 2. Download and preprocess the dataset.\n\nThe ResNet50 script operates on ImageNet 1k, a widely popular image classification dataset from the ILSVRC challenge.\n\nPyTorch can work directly on JPEGs, therefore, preprocessing/augmentation is not needed.\n\nTo train your model using mixed or TF32 precision with Tensor Cores or using FP32,\nperform the following steps using the default parameters of the resnet50 model on the ImageNet dataset.\nFor the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. [Download the images](http://image-net.org/download-images).\n\n2. Extract the training data:\n ```bash\n mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train\n tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar\n find . -name \"*.tar\" | while read NAME ; do mkdir -p \"${NAME%.tar}\"; tar -xvf \"${NAME}\" -C \"${NAME%.tar}\"; rm -f \"${NAME}\"; done\n cd ..\n ```\n\n3. Extract the validation data and move the images to subfolders:\n ```bash\n mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar\n wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash\n ```\n\nThe directory in which the `train/` and `val/` directories are placed, is referred to as `` in this document.\n\n### 3. Build the ResNet50 PyTorch NGC container.\n\n```\ndocker build . -t nvidia_resnet50\n```\n\n### 4. Start an interactive session in the NGC container to run training/inference.\n```\nnvidia-docker run --rm -it -v :/imagenet --ipc=host nvidia_resnet50\n```\n\n### 5. Start training\n\nTo run training for a standard configuration (DGXA100/DGX1V/DGX2V, AMP/TF32/FP32, 90/250 Epochs),\nrun one of the scripts in the `./resnet50v1.5/training` directory\ncalled `./resnet50v1.5/training/{AMP, TF32, FP32}/{ DGXA100, DGX1V, DGX2V }_resnet50_{AMP, TF32, FP32}_{ 90, 250 }E.sh`.\n\nEnsure ImageNet is mounted in the `/imagenet` directory.\n\nExample:\n `bash ./resnet50v1.5/training/AMP/DGX1_resnet50_AMP_250E.sh `\n\n### 6. Start inference\n\nYou can download pretrained weights from NGC:\n\n```bash\nwget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/resnet50_pyt_amp/versions/20.06.0/zip -O resnet50_pyt_amp_20.06.0.zip\n\nunzip resnet50_pyt_amp_20.06.0.zip\n```\n\nTo run inference on ImageNet, run:\n\n`python ./main.py --arch resnet50 --evaluate --epochs 1 --pretrained-from-file nvidia_resnet50_200821.pth.tar -b `\n\nTo run inference on JPEG image using pretrained weights:\n\n`python classify.py --arch resnet50 --pretrained-from-file nvidia_resnet50_200821.pth.tar --precision AMP|FP32 --image `","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\nTo run a non standard configuration use:\n\n* For 1 GPU\n * FP32\n `python ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 `\n `python ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 --amp --static-loss-scale 256 `\n\n* For multiple GPUs\n * FP32\n `python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 `\n * AMP\n `python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 --amp --static-loss-scale 256 `\n\nUse `python ./main.py -h` to obtain the list of available options in the `main.py` script.\n\n### Command-line options:\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:\n\n`python main.py -h`\n\n```\nusage: main.py [-h] [--data-backend BACKEND] [--arch ARCH]\n [--model-config CONF] [-j N] [--epochs N]\n [--run-epochs N] [-b N] [--optimizer-batch-size N] [--lr LR]\n [--lr-schedule SCHEDULE] [--warmup E] [--label-smoothing S]\n [--mixup ALPHA] [--momentum M] [--weight-decay W]\n [--bn-weight-decay] [--nesterov] [--print-freq N]\n [--resume PATH] [--pretrained-from-file PATH]\n [--static-loss-scale STATIC_LOSS_SCALE] [--dynamic-loss-scale]\n [--prof N] [--amp] [--seed SEED] [--gather-checkpoints]\n [--raport-file RAPORT_FILE] [--evaluate] [--training-only]\n [--no-checkpoints] [--checkpoint-filename CHECKPOINT_FILENAME]\n [--workspace DIR] [--memory-format {nchw,nhwc}]\n DIR\n\nPyTorch ImageNet Training\n\npositional arguments:\n DIR path to dataset\n\noptional arguments:\n -h, --help show this help message and exit\n --data-backend BACKEND\n data backend: pytorch | synthetic | dali-gpu | dali-cpu\n (default: dali-cpu)\n --arch ARCH, -a ARCH model architecture: resnet18 | resnet34 | resnet50 |\n resnet101 | resnet152 | resnext50-32x4d |\n resnext101-32x4d | resnext101-32x8d |\n resnext101-32x8d-basic | se-resnext101-32x4d (default:\n resnet50)\n --model-config CONF, -c CONF\n model configs: classic | fanin | grp-fanin | grp-\n fanout(default: classic)\n -j N, --workers N number of data loading workers (default: 5)\n --epochs N number of total epochs to run\n --run-epochs N run only N epochs, used for checkpointing runs\n -b N, --batch-size N mini-batch size (default: 256) per gpu\n --optimizer-batch-size N\n size of a total batch size, for simulating bigger\n batches using gradient accumulation\n --lr LR, --learning-rate LR\n initial learning rate\n --lr-schedule SCHEDULE\n Type of LR schedule: step, linear, cosine\n --warmup E number of warmup epochs\n --label-smoothing S label smoothing\n --mixup ALPHA mixup alpha\n --momentum M momentum\n --weight-decay W, --wd W\n weight decay (default: 1e-4)\n --bn-weight-decay use weight_decay on batch normalization learnable\n parameters, (default: false)\n --nesterov use nesterov momentum, (default: false)\n --print-freq N, -p N print frequency (default: 10)\n --resume PATH path to latest checkpoint (default: none)\n --pretrained-from-file PATH\n load weights from here\n --static-loss-scale STATIC_LOSS_SCALE\n Static loss scale, positive power of 2 values can\n improve amp convergence.\n --dynamic-loss-scale Use dynamic loss scaling. If supplied, this argument\n supersedes --static-loss-scale.\n --prof N Run only N iterations\n --amp Run model AMP (automatic mixed precision) mode.\n --seed SEED random seed used for numpy and pytorch\n --gather-checkpoints Gather checkpoints throughout the training, without\n this flag only best and last checkpoints will be\n stored\n --raport-file RAPORT_FILE\n file in which to store JSON experiment raport\n --evaluate evaluate checkpoint/model\n --training-only do not evaluate\n --no-checkpoints do not store any checkpoints, useful for benchmarking\n --checkpoint-filename CHECKPOINT_FILENAME\n --workspace DIR path to directory where checkpoints will be stored\n --memory-format {nchw,nhwc}\n memory layout, nchw or nhwc\n\n```\n\n### Dataset guidelines\n\nTo use your own dataset, divide it in directories as in the following scheme:\n\n - Training images - `train//`\n - Validation images - `val//`\n\nIf your dataset's has number of classes different than 1000, you need to pass `--num_classes N` flag to the training script.\n\n### Training process\n\nAll the results of the training will be stored in the directory specified with `--workspace` argument.\nScript will store:\n - most recent checkpoint - `checkpoint.pth.tar` (unless `--no-checkpoints` flag is used).\n - checkpoint with best validation accuracy - `model_best.pth.tar` (unless `--no-checkpoints` flag is used).\n - JSON log - in the file specified with `--raport-file` flag.\n\nMetrics gathered through training:\n\n - `train.loss` - training loss\n - `train.total_ips` - training speed measured in images/second\n - `train.compute_ips` - training speed measured in images/second, not counting data loading\n - `train.data_time` - time spent on waiting on data\n - `train.compute_time` - time spent in forward/backward pass\n\nTo restart training from checkpoint use `--resume` option.\n\nTo start training from pretrained weights (e.g. downloaded from NGC) use `--pretrained-from-file` option.\n\nThe difference between those two is that the pretrained weights contain only model weights,\nand checkpoints, apart from model weights, contain optimizer state, LR scheduler state.\n\nCheckpoints are suitable for dividing the training into parts, for example in order\nto divide the training job into shorter stages, or restart training after infrastructure fail.\n\nPretrained weights can be used as a base for finetuning the model to a different dataset,\nor as a backbone to detection models.\n\n### Inference process\n\nValidation is done every epoch, and can be also run separately on a checkpointed model.\n\n`python ./main.py --arch resnet50 --evaluate --epochs 1 --resume -b `\n\nMetrics gathered through training:\n\n - `val.loss` - validation loss\n - `val.top1` - validation top1 accuracy\n - `val.top5` - validation top5 accuracy\n - `val.total_ips` - inference speed measured in images/second\n - `val.compute_ips` - inference speed measured in images/second, not counting data loading\n - `val.data_time` - time spent on waiting on data\n - `val.compute_time` - time spent on inference\n\nTo run inference on JPEG image, you have to first extract the model weights from checkpoint:\n\n`python checkpoint2model.py --checkpoint-path --weight-path `\n\nThen run classification script:\n\n`python classify.py --arch resnet50 --pretrained-from-file --precision AMP|FP32 --image `\n\nYou can also run ImageNet validation on pretrained weights:\n\n`python ./main.py --arch resnet50 --evaluate --epochs 1 --pretrained-from-file -b `\n\n#### NGC Pretrained weights:\n\nPretrained weights can be downloaded from NGC:\n\n```bash\nwget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/resnet50_pyt_amp/versions/20.06.0/zip -O resnet50_pyt_amp_20.06.0.zip\n\nunzip resnet50_pyt_amp_20.06.0.zip\n```\nTo run inference on ImageNet, run:\n\n`python ./main.py --arch resnet50 --evaluate --epochs 1 --pretrained-from-file nvidia_resnet50_200821.pth.tar -b `\n\nTo run inference on JPEG image using pretrained weights:\n\n`python classify.py --arch resnet50 --weights nvidia_resnet50_200821.pth.tar --precision AMP|FP32 --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\nTo benchmark training, run:\n\n* For 1 GPU\n * FP32 (V100 GPUs only)\n `python ./launch.py --model resnet50 --precision FP32 --mode benchmark_training --platform DGX1V --raport-file benchmark.json --epochs 1 --prof 100`\n * TF32 (A100 GPUs only)\n `python ./launch.py --model resnet50 --precision TF32 --mode benchmark_training --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n * AMP\n `python ./launch.py --model resnet50 --precision AMP --mode benchmark_training --platform --raport-file benchmark.json --epochs 1 --prof 100`\n* For multiple GPUs\n * FP32 (V100 GPUs only)\n `python ./launch.py --model resnet50 --precision FP32 --mode benchmark_training --platform DGX1V --raport-file benchmark.json --epochs 1 --prof 100`\n * TF32 (A100 GPUs only)\n `python ./multiproc.py --nproc_per_node 8 ./launch.py --model resnet50 --precision TF32 --mode benchmark_training --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n * AMP\n `python ./multiproc.py --nproc_per_node 8 ./launch.py --model resnet50 --precision AMP --mode benchmark_training --platform --raport-file benchmark.json --epochs 1 --prof 100`\n\nEach of these scripts will run 100 iterations and save results in the `benchmark.json` file.\n\n#### Inference performance benchmark\n\nTo benchmark inference, run:\n\n* FP32 (V100 GPUs only)\n\n`python ./launch.py --model resnet50 --precision FP32 --mode benchmark_inference --platform DGX1V --raport-file benchmark.json --epochs 1 --prof 100`\n\n* TF32 (A100 GPUs only)\n\n`python ./launch.py --model resnet50 --precision TF32 --mode benchmark_inference --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n\n* AMP\n\n`python ./launch.py --model resnet50 --precision AMP --mode benchmark_inference --platform --raport-file benchmark.json --epochs 1 --prof 100`\n\nEach of these scripts will run 100 iterations and save results in the `benchmark.json` file.\n\n### Results\n\n#### Training accuracy results\n\nOur results were obtained by running the applicable training script in the pytorch-20.12 NGC container.\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n\n| **Epochs** | **Mixed Precision Top1** | **TF32 Top1** |\n|:----------:|:------------------------:|:--------------:|\n| 90 | 77.12 +/- 0.11 | 76.95 +/- 0.18 |\n| 250 | 78.43 +/- 0.11 | 78.38 +/- 0.17 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\n| **Epochs** | **Mixed Precision Top1** | **FP32 Top1** |\n|:----------:|:------------------------:|:--------------:|\n| 90 | 76.88 +/- 0.16 | 77.01 +/- 0.16 |\n| 250 | 78.25 +/- 0.12 | 78.30 +/- 0.16 |\n\n##### Training accuracy: NVIDIA DGX-2 (16x V100 32GB)\n\n| **epochs** | **Mixed Precision Top1** | **FP32 Top1** |\n|:-:|:-:|:-:|\n| 50 | 75.81 +/- 0.08 | 76.04 +/- 0.05 |\n| 90 | 77.10 +/- 0.06 | 77.23 +/- 0.04 |\n| 250 | 78.59 +/- 0.13 | 78.46 +/- 0.03 |\n\n##### Example plots\n\nThe following images show a 250 epochs configuration on a DGX-1V.\n\n![ValidationLoss](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/resnet50v1.5//img/loss_plot.png)\n\n\n![ValidationTop1](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/resnet50v1.5//img/top1_plot.png)\n\n\n![ValidationTop5](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/resnet50v1.5//img/top5_plot.png)\n\n\n#### Training performance results\n\nOur results were obtained by running the applicable training script in the pytorch-21.03 NGC container.\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\n| **GPUs** | **Throughput - TF32** | **Throughput - mixed precision** | **Throughput speedup (TF32 to mixed precision)** | **TF32 Strong Scaling** | **Mixed Precision Strong Scaling** | **Mixed Precision Training Time (90E)** | **TF32 Training Time (90E)** |\n|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|:---------------------------------------:|:----------------------------:|\n| 1 | 938 img/s | 2470 img/s | 2.63 x | 1.0 x | 1.0 x | ~14 hours | ~36 hours |\n| 8 | 7248 img/s | 16621 img/s | 2.29 x | 7.72 x | 6.72 x | ~3 hours | ~5 hours |\n\n##### Training performance: NVIDIA DGX-1 16GB (8x V100 16GB)\n\n| **GPUs** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** | **Mixed Precision Training Time (90E)** | **FP32 Training Time (90E)** |\n|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|:---------------------------------------:|:----------------------------:|\n| 1 | 367 img/s | 1200 img/s | 3.26 x | 1.0 x | 1.0 x | ~29 hours | ~92 hours |\n| 8 | 2855 img/s | 8322 img/s | 2.91 x | 7.76 x | 6.93 x | ~5 hours | ~12 hours |\n\n##### Training performance: NVIDIA DGX-1 32GB (8x V100 32GB)\n\n| **GPUs** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** | **Mixed Precision Training Time (90E)** | **FP32 Training Time (90E)** |\n|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|:---------------------------------------:|:----------------------------:|\n| 1 | 356 img/s | 1156 img/s | 3.24 x | 1.0 x | 1.0 x | ~30 hours | ~95 hours |\n| 8 | 2766 img/s | 8056 img/s | 2.91 x | 7.75 x | 6.96 x | ~5 hours | ~13 hours |\n\n#### Inference performance results\n\nOur results were obtained by running the applicable training script in the pytorch-21.03 NGC container.\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\n###### FP32 Inference Latency\n\n| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| 1 | 96 img/s | 10.37 ms | 10.81 ms | 11.73 ms |\n| 2 | 196 img/s | 10.24 ms | 11.18 ms | 12.89 ms |\n| 4 | 386 img/s | 10.46 ms | 11.01 ms | 11.75 ms |\n| 8 | 709 img/s | 11.5 ms | 12.36 ms | 13.12 ms |\n| 16 | 1023 img/s | 16.07 ms | 15.69 ms | 15.97 ms |\n| 32 | 1127 img/s | 29.37 ms | 28.53 ms | 28.67 ms |\n| 64 | 1200 img/s | 55.4 ms | 53.5 ms | 53.71 ms |\n| 128 | 1229 img/s | 109.26 ms | 104.04 ms | 104.34 ms |\n| 256 | 1261 img/s | 214.48 ms | 202.51 ms | 202.88 ms |\n\n###### Mixed Precision Inference Latency\n\n| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| 1 | 78 img/s | 12.78 ms | 13.27 ms | 14.36 ms |\n| 2 | 154 img/s | 13.01 ms | 13.74 ms | 15.19 ms |\n| 4 | 300 img/s | 13.41 ms | 14.25 ms | 15.68 ms |\n| 8 | 595 img/s | 13.65 ms | 14.51 ms | 15.6 ms |\n| 16 | 1178 img/s | 14.0 ms | 15.07 ms | 16.26 ms |\n| 32 | 2146 img/s | 15.84 ms | 17.25 ms | 18.53 ms |\n| 64 | 2984 img/s | 23.18 ms | 21.51 ms | 21.93 ms |\n| 128 | 3249 img/s | 43.55 ms | 39.36 ms | 40.1 ms |\n| 256 | 3382 img/s | 84.14 ms | 75.3 ms | 80.08 ms |\n\n##### Inference performance: NVIDIA T4\n\n###### FP32 Inference Latency\n\n| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| 1 | 98 img/s | 10.7 ms | 12.82 ms | 16.71 ms |\n| 2 | 186 img/s | 11.26 ms | 13.79 ms | 16.99 ms |\n| 4 | 325 img/s | 12.73 ms | 13.89 ms | 18.03 ms |\n| 8 | 363 img/s | 22.41 ms | 22.57 ms | 22.9 ms |\n| 16 | 409 img/s | 39.77 ms | 39.8 ms | 40.23 ms |\n| 32 | 420 img/s | 77.62 ms | 76.92 ms | 77.28 ms |\n| 64 | 428 img/s | 152.73 ms | 152.03 ms | 153.02 ms |\n| 128 | 426 img/s | 309.26 ms | 303.38 ms | 305.13 ms |\n| 256 | 415 img/s | 635.98 ms | 620.16 ms | 625.21 ms |\n\n###### Mixed Precision Inference Latency\n\n| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| 1 | 79 img/s | 12.96 ms | 15.47 ms | 20.0 ms |\n| 2 | 156 img/s | 13.18 ms | 14.9 ms | 18.73 ms |\n| 4 | 317 img/s | 12.99 ms | 14.69 ms | 19.05 ms |\n| 8 | 652 img/s | 12.82 ms | 16.04 ms | 19.43 ms |\n| 16 | 1050 img/s | 15.8 ms | 16.57 ms | 20.62 ms |\n| 32 | 1128 img/s | 29.54 ms | 28.79 ms | 28.97 ms |\n| 64 | 1165 img/s | 57.41 ms | 55.67 ms | 56.11 ms |\n| 128 | 1190 img/s | 114.24 ms | 109.17 ms | 110.41 ms |\n| 256 | 1198 img/s | 225.95 ms | 215.28 ms | 222.94 ms |","hasPerformance",[],{"_86":241,"_114":242,"_115":-5,"_123":35,"_122":35,"_124":240,"_222":91},{"_86":132,"_114":132,"_115":-5,"_123":35,"_122":35,"_124":240,"_222":91},{"_86":29,"_114":29,"_115":-5,"_123":35,"_122":35,"_124":239,"_222":91},[],[],"-","Latest (21.03.1)","nvidia/dle/resnet_pyt","key","nvidia/dle/resources/resnet_pyt","resourceId","resnet_pyt","shortDescription","With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.","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/convnets-pyt)\n\nThe ResNet50 v1.5 model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385).\n\nThe difference between v1 and v1.5 is that, in the bottleneck blocks which requires\ndownsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.\n\nThis difference makes ResNet50 v1.5 slightly more accurate (\\~0.5% top1) than v1, but comes with a smallperformance drawback (\\~5% imgs/sec).\n\nThe model is initialized as described in [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf)\n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 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.\n\nWe are currently working on adding [NHWC data layout](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) support for Mixed Precision training.\n\n### Default configuration\n\nThe following sections highlight the default configurations for the ResNet50 model.\n\n#### Optimizer\n\nThis model uses SGD with momentum optimizer with the following hyperparameters:\n\n* Momentum (0.875)\n* Learning rate (LR) = 0.256 for 256 batch size, for other batch sizes we linearly\nscale the learning rate.\n* Learning rate schedule - we use cosine LR schedule\n* For bigger batch sizes (512 and up) we use linear warmup of the learning rate\nduring the first couple of epochs\naccording to [Training ImageNet in 1 hour](https://arxiv.org/abs/1706.02677).\nWarmup length depends on the total training length.\n* Weight decay (WD)= 3.0517578125e-05 (1/32768).\n* We do not apply WD on Batch Norm trainable parameters (gamma/bias)\n* Label smoothing = 0.1\n* We train for:\n * 50 Epochs -> configuration that reaches 75.9% top1 accuracy\n * 90 Epochs -> 90 epochs is a standard for ImageNet networks\n * 250 Epochs -> best possible accuracy.\n* For 250 epoch training we also use [MixUp regularization](https://arxiv.org/pdf/1710.09412.pdf).\n\n#### Data augmentation\n\nThis model uses the following data augmentation:\n\n* For training:\n * Normalization\n * Random resized crop to 224x224\n * Scale from 8% to 100%\n * Aspect ratio from 3/4 to 4/3\n * Random horizontal flip\n* For inference:\n * Normalization\n * Scale to 256x256\n * Center crop to 224x224\n\n#### Other training recipes\n\nThis script does not target any specific benchmark.\nThere are changes that others have made which can speed up convergence and/or increase accuracy.\n\nOne of the more popular training recipes is provided by [fast.ai](https://github.com/fastai/imagenet-fast).\n\nThe fast.ai recipe introduces many changes to the training procedure, one of which is progressive resizing of the training images.\n\nThe first part of training uses 128px images, the middle part uses 224px images, and the last part uses 288px images.\nThe final validation is performed on 288px images.\n\nTraining script in this repository performs validation on 224px images, just like the original paper described.\n\nThese two approaches can't be directly compared, since the fast.ai recipe requires validation on 288px images,\nand this recipe keeps the original assumption that validation is done on 224px images.\n\nUsing 288px images means that a lot more FLOPs are needed during inference to reach the same accuracy.\n\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| Feature | ResNet50\n|-----------------------|--------------------------\n|[DALI](https://docs.nvidia.com/deeplearning/sdk/dali-release-notes/index.html) | Yes\n|[APEX AMP](https://nvidia.github.io/apex/amp.html) | Yes |\n\n#### Features\n\n- NVIDIA DALI - DALI is a library accelerating data preparation pipeline. To accelerate your input pipeline, you only need to define your data loader\nwith the DALI library. For more information about DALI, refer to the [DALI product documentation](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html).\n\n- [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as [Automatic Mixed Precision (AMP)](https://nvidia.github.io/apex/amp.html), which require minimal network code changes to leverage Tensor Cores performance. Refer to the [Enabling mixed precision](#enabling-mixed-precision) section for more details.\n\n### DALI\n\nWe use [NVIDIA DALI](https://github.com/NVIDIA/DALI),\nwhich speeds up data loading when CPU becomes a bottleneck.\nDALI can use CPU or GPU, and outperforms the PyTorch native dataloader.\n\nRun training with `--data-backends dali-gpu` or `--data-backends dali-cpu` to enable DALI.\nFor DGXA100 and DGX1 we recommend `--data-backends dali-cpu`, for DGX2 we recommend `--data-backends dali-gpu`.\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training 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 in the Pascal architecture and first supported in CUDA 8 in 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) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).\n\n#### Enabling mixed precision\n\nMixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), a library from [APEX](https://github.com/NVIDIA/apex) that casts variables to half-precision upon retrieval,\nwhile storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients.\nIn PyTorch, loss scaling can be easily applied by using scale_loss() method provided by AMP. The scaling value to be used can be [dynamic](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.DynamicLossScaler) or fixed.\n\nFor an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance.\n\nTo enable mixed precision, you can:\n- Import AMP from APEX:\n\n ```python\n from apex import amp\n ```\n\n- Wrap model and optimizer in amp.initialize:\n\n ```python\n model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\", loss_scale=\"dynamic\")\n ```\n\n- Scale loss before backpropagation:\n ```python\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\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","ResNet50 v1.5 for PyTorch",["D",1667525001109],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/resnet_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",{"_157":1261,"_159":1262},"ngcApps",[1214],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1195":1196,"_1197":1198,"_1199":1200,"_1201":1202,"_1203":293,"_110":319,"_1204":1205,"_1206":59,"_1207":91,"_1208":91,"_1209":1210,"_1211":91,"_1212":1213},"amplitude",{"_1192":1193,"_833":-7,"_1194":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1186":1187,"_1188":1189,"_1190":1191},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788479220161,"1.15.0","defaultSearchSuggestions",{"_1073":400,"_1074":1075,"_1076":1077,"_1078":1079},"announcements",[],"consentState",{"_357":1063,"_1064":91,"_1065":35,"_1066":35,"_1067":1068},"consentConfig",{"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358},"showContactAdmin",1788479220162,[],[],[333,334,335,336,337,338],{"_49":63,"_51":74,"_53":152,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":152,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":68,"_53":152,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":152,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":152,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":152,"_55":-5,"_57":-5,"_58":59},"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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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[450],{"_114":451,"_405":448,"_406":35,"_216":452},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614],[561,562,563,564,565],{"_398":474,"_251":474,"_216":400,"_371":585},{"_398":448,"_251":448,"_216":400,"_371":580},{"_398":575,"_251":575,"_216":400,"_371":576},{"_398":570,"_251":570,"_216":400,"_371":571},{"_398":566,"_251":566,"_216":400,"_371":567},"nvidia.partners",[568],{"_114":569,"_405":566,"_406":35,"_216":442},"edge","app.hushly.com",[572],{"_114":573,"_405":570,"_406":35,"_216":574},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[577],{"_114":578,"_405":575,"_406":35,"_216":579},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701],[653,654,655,656],{"_398":668,"_251":668,"_216":400,"_371":669},{"_398":663,"_251":663,"_216":400,"_371":664},{"_398":461,"_251":461,"_216":400,"_371":660},{"_398":575,"_251":575,"_216":400,"_371":657},[658],{"_114":659,"_405":575,"_406":91,"_216":579},"_gh_sess",[661],{"_114":662,"_405":461,"_406":91,"_216":468},"YSC","bizible.com",[665],{"_114":666,"_405":663,"_406":35,"_216":667},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[670],{"_114":671,"_405":668,"_406":35,"_216":672},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_114":743,"_405":520,"_406":35,"_216":744},{"_114":728,"_405":742,"_406":91,"_216":729},{"_114":711,"_405":523,"_406":35,"_216":636},{"_114":711,"_405":624,"_406":35,"_216":636},{"_114":740,"_405":520,"_406":35,"_216":741},{"_114":728,"_405":526,"_406":91,"_216":729},{"_114":738,"_405":520,"_406":35,"_216":739},{"_114":728,"_405":523,"_406":91,"_216":729},{"_114":736,"_405":623,"_406":35,"_216":737},{"_114":728,"_405":645,"_406":91,"_216":729},{"_114":708,"_405":523,"_406":35,"_216":710},{"_114":734,"_405":520,"_406":91,"_216":735},{"_114":732,"_405":520,"_406":35,"_216":733},{"_114":730,"_405":520,"_406":91,"_216":731},{"_114":728,"_405":619,"_406":91,"_216":729},{"_114":726,"_405":520,"_406":35,"_216":727},{"_114":724,"_405":520,"_406":35,"_216":725},{"_114":722,"_405":520,"_406":91,"_216":723},{"_114":720,"_405":628,"_406":35,"_216":721},{"_114":708,"_405":619,"_406":35,"_216":710},{"_114":718,"_405":520,"_406":35,"_216":719},{"_114":716,"_405":520,"_406":91,"_216":717},{"_114":714,"_405":520,"_406":35,"_216":715},{"_114":712,"_405":520,"_406":35,"_216":713},{"_114":711,"_405":520,"_406":35,"_216":636},{"_114":708,"_405":709,"_406":35,"_216":710},{"_114":706,"_405":520,"_406":35,"_216":707},{"_114":704,"_405":520,"_406":91,"_216":705},{"_114":702,"_405":520,"_406":35,"_216":703},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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	Files	Compressed Size		Download
21.03.1	1	36.59 KB	November 4, 2022 UTC
21.03.0Selected	1	36.65 KB	September 22, 2022 UTC