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README.md
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deeplabv3.py
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fcn.py
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intermediate_layer_getter.py
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model.py
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README.md

Image Segmentation using torchvision's pretrained deeplabv3_resnet_101_coco model.

  • Download the pre-trained deeplabv3_resnet_101_coco image segmentation model's state_dict from the following URL:

https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth

wget https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth

  • Create a model archive file and serve the deeplabv3 model in TorchServe using below commands

    torch-model-archiver --model-name deeplabv3_resnet_101 --version 1.0 --model-file examples/image_segmenter/deeplabv3/model.py --serialized-file deeplabv3_resnet101_coco-586e9e4e.pth --handler image_segmenter --extra-files examples/image_segmenter/deeplabv3/deeplabv3.py,examples/image_segmenter/deeplabv3/intermediate_layer_getter.py,examples/image_segmenter/deeplabv3/fcn.py
mkdir model_store
mv deeplabv3_resnet_101.mar model_store/
torchserve --start --model-store model_store --models deeplabv3=deeplabv3_resnet_101.mar
curl http://127.0.0.1:8080/predictions/deeplabv3 -T examples/image_segmenter/persons.jpg

  • Output An array of shape [Batch, Height, Width, 2] where the final dimensions are [class, probability]

[[[0.0, 0.9988763332366943], [0.0, 0.9988763332366943], [0.0, 0.9988763332366943], [0.0, 0.9988763332366943], [0.0, 0.9988666772842407], [0.0, 0.9988440275192261], [0.0, 0.9988170862197876], [0.0, 0.9987859725952148] ... ]]