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Changelog

October 2019

Initial release

July 2020

Added A100 scripts
Updated README

Known issues

There are no known issues with this model.

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The ResNeXt101-32x4d is a model introduced in the Aggregated Residual Transformations for Deep Neural Networks paper.

It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions.

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 3x 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 use NHWC data layout when training using Mixed Precision.

Model architecture

$\"ResNextArch\"$

Image source: Aggregated Residual Transformations for Deep Neural Networks

Image shows difference between ResNet bottleneck block and ResNeXt bottleneck block.

ResNeXt101-32x4d model's cardinality equals to 32 and bottleneck width equals to 4.

Default configuration

The following sections highlight the default configurations for the ResNeXt101-32x4d 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)= 6.103515625e-05 (1/16384).
We do not apply WD on Batch Norm trainable parameters (gamma/bias)
Label smoothing = 0.1
We train for:\n
- 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

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	ResNeXt101-32x4d
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.

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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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 ResNeXt101-32x4d 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 resnext101-32x4d 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 ResNeXt101-32x4d PyTorch NGC container.\n\n```\ndocker build . -t nvidia_resnext101-32x4d\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_resnext101-32x4d\n```\n\n### 5. Start training\n\nTo run training for a standard configuration (DGXA100/DGX1V, AMP/TF32/FP32, 90/250 Epochs),\nrun one of the scripts in the `./resnext101-32x4d/training` directory\ncalled `./resnext101-32x4d/training/{AMP, TF32, FP32}/{ DGXA100, DGX1V }_resnext101-32x4d_{AMP, TF32, FP32}_{ 90, 250 }E.sh`.\n\nEnsure ImageNet is mounted in the `/imagenet` directory.\n\nExample:\n `bash ./resnext101-32x4d/training/AMP/DGX1_resnext101-32x4d_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/resnext101_32x4d_pyt_amp/versions/20.06.0/zip -O resnext101_32x4d_pyt_amp_20.06.0.zip\n\nunzip resnext101_32x4d_pyt_amp_20.06.0.zip\n```\n\nTo run inference on ImageNet, run:\n\n`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-from-file nvidia_resnext101-32x4d_200821.pth.tar -b `\n\nTo run inference on JPEG image using pretrained weights:\n\n`python classify.py --arch resnext101-32x4d --pretrained-from-file nvidia_resnext101-32x4d_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 resnext101-32x4d -c fanin --label-smoothing 0.1 `\n `python ./main.py --arch resnext101-32x4d -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 resnext101-32x4d -c fanin --label-smoothing 0.1 `\n * AMP\n `python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d -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] [--num-classes N] [-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 | syntetic | 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 --num-classes N number of classes in the dataset\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### 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 resnext101-32x4d --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 resnext101-32x4d --pretrained-from-file --precision AMP|FP32 --image `\n\nYou can also run ImageNet validation on pretrained weights:\n\n`python ./main.py --arch resnext101-32x4d --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/resnext101_32x4d_pyt_amp/versions/20.06.0/zip -O resnext101_32x4d_pyt_amp_20.06.0.zip\n\nunzip resnext101_32x4d_pyt_amp_20.06.0.zip\n```\n\nTo run inference on ImageNet, run:\n\n`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-from-file nvidia_resnext101-32x4d_200821.pth.tar -b `\n\nTo run inference on JPEG image using pretrained weights:\n\n`python classify.py --arch resnext101-32x4d --pretrained-from-file nvidia_resnext101-32x4d_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 resnext101-32x4d --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 resnext101-32x4d --precision TF32 --mode benchmark_training --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n * AMP\n `python ./launch.py --model resnext101-32x4d --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 resnext101-32x4d --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 resnext101-32x4d --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 resnext101-32x4d --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 resnext101-32x4d --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 resnext101-32x4d --precision TF32 --mode benchmark_inference --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n\n* AMP\n\n`python ./launch.py --model resnext101-32x4d --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 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 | 79.47 +/- 0.03 | 79.38 +/- 0.07 |\n| 250 | 80.19 +/- 0.08 | 80.27 +/- 0.1 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\n| **Epochs** | **Mixed Precision Top1** | **FP32 Top1** |\n|:----------:|:------------------------:|:--------------:|\n| 90 | 79.49 +/- 0.05 | 79.40 +/- 0.10 |\n| 250 | 80.26 +/- 0.11 | 80.06 +/- 0.06 |\n\n##### Example plots\n\nThe following images show a 250 epochs configuration on a DGX-1V.\n\n![ValidationLoss](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/Classification/ConvNets/resnext101-32x4d/img/loss_plot.png)\n\n\n![ValidationTop1](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/Classification/ConvNets/resnext101-32x4d/img/top1_plot.png)\n\n\n![ValidationTop5](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/Classification/ConvNets/resnext101-32x4d/img/top5_plot.png)\n\n\n#### Training performance results\n\nOur results were obtained by running the applicable training script 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 | 456 img/s | 1211 img/s | 2.65 x | 1.0 x | 1.0 x | ~28 hours | ~74 hours |\n| 8 | 3471 img/s | 7925 img/s | 2.28 x | 7.6 x | 6.54 x | ~5 hours | ~10 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 | 147 img/s | 587 img/s | 3.97 x | 1.0 x | 1.0 x | ~58 hours | ~228 hours |\n| 8 | 1133 img/s | 4065 img/s | 3.58 x | 7.65 x | 6.91 x | ~9 hours | ~30 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 | 144 img/s | 565 img/s | 3.9 x | 1.0 x | 1.0 x | ~60 hours | ~233 hours |\n| 8 | 1108 img/s | 3863 img/s | 3.48 x | 7.66 x | 6.83 x | ~9 hours | ~31 hours |\n\n#### Inference performance results\n\nOur results were obtained by running the applicable training script 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 | 55 img/s | 17.95 ms | 20.61 ms | 22.0 ms |\n| 2 | 105 img/s | 19.2 ms | 20.74 ms | 22.77 ms |\n| 4 | 170 img/s | 23.65 ms | 24.66 ms | 28.0 ms |\n| 8 | 336 img/s | 24.05 ms | 24.92 ms | 27.75 ms |\n| 16 | 397 img/s | 40.77 ms | 40.44 ms | 40.65 ms |\n| 32 | 452 img/s | 72.12 ms | 71.1 ms | 71.35 ms |\n| 64 | 500 img/s | 130.9 ms | 128.19 ms | 128.64 ms |\n| 128 | 527 img/s | 249.57 ms | 242.77 ms | 243.63 ms |\n| 256 | 533 img/s | 496.76 ms | 478.04 ms | 480.42 ms |\n\n###### Mixed Precision Inference Latency\n\n| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| 1 | 43 img/s | 23.08 ms | 24.18 ms | 27.82 ms |\n| 2 | 84 img/s | 23.65 ms | 24.64 ms | 27.87 ms |\n| 4 | 164 img/s | 24.38 ms | 27.33 ms | 27.95 ms |\n| 8 | 333 img/s | 24.18 ms | 25.92 ms | 28.3 ms |\n| 16 | 640 img/s | 25.4 ms | 26.53 ms | 29.47 ms |\n| 32 | 1195 img/s | 27.72 ms | 29.9 ms | 32.19 ms |\n| 64 | 1595 img/s | 41.89 ms | 40.15 ms | 41.08 ms |\n| 128 | 1699 img/s | 79.45 ms | 75.65 ms | 76.08 ms |\n| 256 | 1746 img/s | 154.68 ms | 145.76 ms | 146.52 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 | 56 img/s | 18.18 ms | 20.45 ms | 24.58 ms |\n| 2 | 109 img/s | 18.77 ms | 21.53 ms | 26.21 ms |\n| 4 | 151 img/s | 26.89 ms | 27.81 ms | 30.94 ms |\n| 8 | 164 img/s | 48.99 ms | 49.44 ms | 49.91 ms |\n| 16 | 172 img/s | 93.51 ms | 93.73 ms | 94.16 ms |\n| 32 | 180 img/s | 178.83 ms | 178.41 ms | 179.07 ms |\n| 64 | 178 img/s | 361.95 ms | 360.7 ms | 362.32 ms |\n| 128 | 172 img/s | 756.93 ms | 750.21 ms | 752.45 ms |\n| 256 | 161 img/s | 1615.79 ms | 1580.61 ms | 1583.43 ms |\n\n###### Mixed Precision Inference Latency\n\n| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| 1 | 44 img/s | 23.0 ms | 25.77 ms | 29.41 ms |\n| 2 | 87 img/s | 23.14 ms | 26.55 ms | 30.97 ms |\n| 4 | 178 img/s | 22.8 ms | 24.2 ms | 29.38 ms |\n| 8 | 371 img/s | 21.98 ms | 25.34 ms | 29.61 ms |\n| 16 | 553 img/s | 29.47 ms | 29.52 ms | 31.14 ms |\n| 32 | 578 img/s | 56.56 ms | 56.04 ms | 56.37 ms |\n| 64 | 591 img/s | 110.82 ms | 109.37 ms | 109.83 ms |\n| 128 | 597 img/s | 220.44 ms | 215.33 ms | 216.3 ms |\n| 256 | 598 img/s | 439.3 ms | 428.2 ms | 431.46 ms |","hasPerformance","policies",[],{"_181":212,"_183":213,"_139":-5,"_147":26,"_149":26,"_163":211,"_150":63},{"_181":210,"_183":210,"_139":-5,"_147":26,"_149":26,"_163":211,"_150":63},{"_181":208,"_183":208,"_139":-5,"_147":26,"_149":26,"_163":209,"_150":63},{"_181":206,"_183":206,"_139":-5,"_147":26,"_149":26,"_163":207,"_150":63},{"_181":204,"_183":204,"_139":-5,"_147":26,"_149":26,"_163":205,"_150":63},{"_181":202,"_183":202,"_139":-5,"_147":26,"_149":26,"_163":203,"_150":63},{"_181":200,"_183":200,"_139":-5,"_147":26,"_149":26,"_163":201,"_150":63},{"_181":198,"_183":198,"_139":-5,"_147":26,"_149":26,"_163":199,"_150":63},{"_181":196,"_183":196,"_139":-5,"_147":26,"_149":26,"_163":197,"_150":63},{"_181":194,"_183":194,"_139":-5,"_147":26,"_149":26,"_163":195,"_150":63},{"_181":192,"_183":192,"_139":-5,"_147":26,"_149":26,"_163":193,"_150":63},{"_181":14,"_183":14,"_139":-5,"_147":26,"_149":26,"_163":191,"_150":63},{"_181":189,"_183":189,"_139":-5,"_147":26,"_149":26,"_163":190,"_150":63},{"_181":187,"_183":187,"_139":-5,"_147":26,"_149":26,"_163":188,"_150":63},{"_181":185,"_183":185,"_139":-5,"_147":26,"_149":26,"_163":186,"_150":63},{"_181":182,"_183":182,"_139":-5,"_147":26,"_149":26,"_163":184,"_150":63},"id","1","name",[],"20.06.0",[],"20.06.1",[],"20.07.1",[],[],"20.12.1",[],"21.03.0",[],"21.03.1",[],"21.03.3",[],"21.03.4",[],"21.03.2",[],"20.12.0",[],"20.07.0",[],"21.03.5",[],"21.03.9",[],"-","Latest (21.03.9)","nvidia/resnext_for_pytorch","key","nvidia/resources/resnext_for_pytorch","resourceId","resnext_for_pytorch","shortDescription","ResNet with bottleneck 3x3 Convolutions substituted by 3x3 Grouped Convolutions.","The ResNeXt101-32x4d is a model introduced in the [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf) paper.\n\nIt is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions.\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 3x 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 use [NHWC data layout](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) when training using Mixed Precision.\n\n### Model architecture\n\n![ResNextArch](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/Classification/ConvNets/resnext101-32x4d/img/ResNeXtArch.png)\n\n\n_Image source: [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf)_\n\nImage shows difference between ResNet bottleneck block and ResNeXt bottleneck block.\n\nResNeXt101-32x4d model's cardinality equals to 32 and bottleneck width equals to 4.\n\n### Default configuration\n\nThe following sections highlight the default configurations for the ResNeXt101-32x4d 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)= 6.103515625e-05 (1/16384).\n* We do not apply WD on Batch Norm trainable parameters (gamma/bias)\n* Label smoothing = 0.1\n* We train for:\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### Feature support matrix\n\nThe following features are supported by this model:\n\n| Feature | ResNeXt101-32x4d\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`.\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","ResNeXt101-32x4d for PyTorch",["D",1680632324358],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/resnext_for_pytorch","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",2065389,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[255,256],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_222":266,"_258":267,"_260":268,"_262":263},{"_222":257,"_258":259,"_260":261,"_262":263},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_264":265,"_258":259},"category","label","field","platform","Computer Vision","soln_computer_vision",{"_264":269,"_258":267},"technology",{"_84":1447,"_86":1448},"ngcApps",[1400],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":281,"_111":305,"_1391":1392,"_1380":50,"_1393":63,"_1394":63,"_1395":1396,"_1397":63,"_1398":1399},"amplitude",{"_1378":1379,"_1380":50,"_980":-7,"_1381":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1372":1373,"_1374":1375,"_1376":1377},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1255":386,"_1256":1257,"_1258":1259,"_1260":1261},"announcements",[],"consentState",{"_343":1245,"_1246":63,"_1247":26,"_1248":26,"_1249":1250},"consentConfig",{"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344},"showContactAdmin",1783404257644,[],[],[319,320,321,322,323,324],{"_39":53,"_41":60,"_43":79,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":59,"_43":79,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":58,"_43":79,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":79,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":79,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":79,"_45":-5,"_47":-5,"_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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Information collected with these cookies is used to measure and find ways to improve website 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