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.

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 Converter:

## Legal Information

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