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: