1. Clone the repository.

git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/Classification/

2. Download and preprocess the dataset.

The ResNet50 script operates on ImageNet 1k, a widely popular image classification dataset from the ILSVRC challenge.

PyTorch can work directly on JPEGs, therefore, preprocessing/augmentation is not needed.

To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the resnet50 model on the ImageNet dataset. For the specifics concerning training and inference, see the Advanced section.

1. Download the images.

2. Extract the training data:

mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
cd ..

3. Extract the validation data and move the images to subfolders:

mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash

The directory in which the train/ and val/ directories are placed, is referred to as <path to imagenet> in this document.

3. Build the ResNet50 PyTorch NGC container.

docker build . -t nvidia_resnet50

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

nvidia-docker run --rm -it -v <path to imagenet>:/imagenet --ipc=host nvidia_resnet50

5. Start training

To run training for a standard configuration (DGXA100/DGX1V/DGX2V, AMP/TF32/FP32, 90/250 Epochs), run one of the scripts in the ./resnet50v1.5/training directory called ./resnet50v1.5/training/{AMP, TF32, FP32}/{ DGXA100, DGX1V, DGX2V }_resnet50_{AMP, TF32, FP32}_{ 90, 250 }E.sh.

Ensure ImageNet is mounted in the /imagenet directory.

Example: bash ./resnet50v1.5/training/AMP/DGX1_resnet50_AMP_250E.sh <path were to store checkpoints and logs>

6. Start inference

You can download pretrained weights from NGC:

wget --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

unzip resnet50_pyt_amp_20.06.0.zip

To run inference on ImageNet, run:

python ./main.py --arch resnet50 --evaluate --epochs 1 --pretrained-from-file nvidia_resnet50_200821.pth.tar -b <batch size> <path to imagenet>

To run inference on JPEG image using pretrained weights:

python classify.py --arch resnet50 --pretrained-from-file nvidia_resnet50_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>