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Merge pull request NVIDIA#54 from vinhngx/master
adding resume capability for wget in case data downloads stop early
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‎PyTorch/Segmentation/MaskRCNN/README.md‎

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@@ -32,7 +32,7 @@ Mask R-CNN is a convolution based neural network for the task of object instance
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The repository also contains scripts to interactively launch training, benchmarking and inference routines in a Docker container.
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The major differences between the official implementation of the paper and our version of Mask R-CNN are as follows:
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- Mixed precision support with [PyTorch AMP](https://www.google.com/url?q=https://github.com/NVIDIA/apex&sa=D&ust=1552333999336000&usg=AFQjCNGO8dE0mQMO0lUfhMyCs7g0NmJXkw).
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- Mixed precision support with [PyTorch AMP](https://github.com/NVIDIA/apex).
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- Gradient accumulation to simulate larger batches.
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- Custom fused CUDA kernels for faster computations.
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To download, verify, and extract the COCO dataset, use the following scripts:
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```
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cd Detectron_PyT
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./download_dataset.sh <data/dir>
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../download_dataset.sh <data/dir>
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```
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By default, the data is organized into the following structure:
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```

‎PyTorch/Segmentation/MaskRCNN/download_dataset.sh‎

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DATA_DIR=$1
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wget https://dl.fbaipublicfiles.com/detectron/coco/coco_annotations_minival.tgz
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wget http://images.cocodataset.org/zips/train2014.zip
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wget http://images.cocodataset.org/zips/val2014.zip
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wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip
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wget -c https://dl.fbaipublicfiles.com/detectron/coco/coco_annotations_minival.tgz
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wget -c http://images.cocodataset.org/zips/train2014.zip
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wget -c http://images.cocodataset.org/zips/val2014.zip
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wget -c http://images.cocodataset.org/annotations/annotations_trainval2014.zip
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if md5sum -c hashes.md5
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then

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