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⚠️ Notice: Limited Maintenance

This project is no longer actively maintained. While existing releases remain available, there are no planned updates, bug fixes, new features, or security patches. Users should be aware that vulnerabilities may not be addressed.

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 --disable-token-auth  --enable-model-api
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] ... ]]