ResNet v1.5 for MXNet

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NVIDIA

ResNet v1.5 for MXNet

With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.

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 ResNet-50 model on the ImageNet 1k dataset. For the specifics concerning training and inference, see the Advanced section.

Clone the repository.

git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/MxNet/Classification/RN50v1.5

Build the ResNet-50 MXNet NGC container.

After Docker is set up, you can build the ResNet-50 image with:

docker build . -t nvidia_rn50_mx

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

nvidia-docker run --rm -it --ipc=host -v :/data/imagenet/train-val-recordio-passthrough nvidia_rn50_mx

Download the data.

Download the images from http://image-net.org/download-images.
Extract the training and validation 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 ..
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

Preprocess the ImageNet 1k dataset.

./scripts/prepare_imagenet.sh

Start training.

./runner -n -b

Start validation/evaluation.

./runner -n -b --load --mode val

Start inference/predictions.

./runner --load --mode pred --data-pred