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pspnet-pytorch

Use Case and High-Level Description

pspnet-pytorch is a semantic segmentation model, pre-trained on Pascal VOC dataset for 21 object classes, listed in <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt file. The model was built on ResNetV1-50 backbone and PSP segmentation head. This model is used for pixel-level prediction tasks. For details see repository, paper.

Specification

Metric

Value

Type

Semantic segmentation

GFlops

357.1719

MParams

46.5827

Source framework

PyTorch*

Accuracy

Metric

Value

mean_iou

70.6%

Accuracy metrics were obtained with fixed input resolution 512x512.

Input

Original model

Image, name: input.1, shape: 1, 3, 512, 512, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: RGB. Mean values: [123.675, 116.28, 103.53], scale values: [58.395, 57.12, 57.375]

Converted Model

Image, name: input.1, shape: 1, 3, 512, 512, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Output

Original Model

Integer values in a range [0, 20], which represent an index of a predicted class for each image pixel. Name: segmentation_map, shape: 1, 1, 512, 512 in B, 1, H, W format, where:

  • B - batch size

  • H - image height

  • W - image width

Converted Model

Integer values in a range [0, 20], which represent an index of a predicted class for each image pixel. Name: segmentation_map, shape: 1, 1, 512, 512 in B, 1, H, W format, where:

  • B - batch size

  • H - image height

  • W - image width

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
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