f3net¶

Use Case and High-Level Description¶

F3Net: Fusion, Feedback and Focus for Salient Object Detection. For details see the repository, paper

Specification¶

Metric	Value
Type	Salient object detection
GFLOPs	31.2883
MParams	25.2791
Source framework	PyTorch*

Accuracy¶

Metric	Value
F-measure	84.21%

The F-measure estimated on Pascal-S dataset and defined as the weighted harmonic mean of precision and recall.

F-measure = (1 + β^2) * (Precision * Recall) / (β^2 * (Precision + Recall))

Empirically, β^2 is set to 0.3 to put more emphasis on precision.

Precision and Recall are calculated based on the binarized salient object mask and ground-truth:

Precision = TP / TP + FP, Recall = TP / TP + FN,

where TP, TN, FP, FN denote true-positive, true-negative, false-positive, and false-negative respectively. More details regarding evaluation procedure can be found in this paper

Input¶

Original model¶

Image, name - input.1, shape - 1, 3, 352, 352, 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 - [124.55, 118.90, 102.94] Scale values - [56.77, 55.97, 57.50]

Converted model¶

Image, name - input.1, shape - 1, 3, 352, 352, 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¶

Saliency map, name saliency_map, shape 1, 1, 352, 352, format B, C, H, W, where:

B - batch size
C - number of channels
H - image height
W - image width

Sigmoid function should be applied on saliency map for conversion probability into [0, 1] range.

Converted model¶

The converted model has the same parameters as the original model.

Download a Model and Convert it into OpenVINO™ IR Format¶

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

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage¶

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

Image Segmentation Python* Demo

Legal Information¶

The original model is distributed under the following license:

MIT License

Copyright (c) 2019 Jun Wei

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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