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.
Metric | Value |
---|---|
Type | Semantic segmentation |
GFlops | 357.1719 |
MParams | 46.5827 |
Source framework | PyTorch* |
Metric | Value |
---|---|
mean_iou | 70.6% |
Accuracy metrics were obtained with fixed input resolution 512x512.
Image, name: input.1
, shape: 1, 3, 512, 512
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: RGB
.
Mean values: [123.675, 116.28, 103.53], scale values: [58.395, 57.12, 57.375]
Image, name: input.1
, shape: 1, 3, 512, 512
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: BGR
.
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 sizeH
- image heightW
- image width
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 sizeH
- image heightW
- image width
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>
The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-MMSegmentation-Models.txt.