yolact-resnet50-fpn-pytorch

## Use Case and High-Level Description

YOLACT ResNet 50 is a simple, fully convolutional model for real-time instance segmentation described in "YOLACT: Real-time Instance Segmentation" paper. Model pre-trained in Pytorch* on Common Objects in Context (COCO) dataset. For details, see the repository.

## Specification

Metric Value
Type Instance segmentation
GFlops 118.575
MParams 36.829
Source framework PyTorch*

## Accuracy

Metric Value
AP@masks 28.00%
AP@boxes 30.69%

## Input

### Original Model

Image, name: input.1, shape: 1, 3, 550, 550, format: B, C, H, W, where:

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

Expected color order: RGB. Mean values - [123.675, 116.78, 103.94], scale values - [58.395, 57.12, 57.375].

### Converted Model

Image, name: input.1, shape: 1, 3, 550, 550, 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

1. Detection scores, name: conf. Contains score distribution over all classes in the [0,1] range . The model was trained on Common Objects in Context (COCO) dataset version with 80 categories of objects, 0 class is for background. Output shape is 1, 19248, 81 in B, N, C format, where:
• B - batch size,
• N - number of detected boxes,
• C - number of classes.
2. Detection boxes, name: boxes. Contains detection boxes coordinates in a format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates of the top left corner, (x_max, y_max) are coordinates of the right bottom corner. Coordinates are normalized in [0, 1] range. Output shape is 1, 19248, 4 in B, N, 4 format, where:
• B - batch size,
• N - number of detected boxes.
3. Masks features prototypes, name: proto. Contains the features projection for instance mask decoding. Output shape is 1, 138, 138, 32 in B, H, W, C, where:
• B - batch size,
• H - mask height,
• W - mask width,
• C - channels.
4. Raw instance masks, name: mask. Contains segmentation heatmaps of detected objects for all classes for every output bounding box. Output shape is B, N, C format, where:
• B - batch size,
• N - number of detected boxes,
• C - channels.

Final mask prediction can be obtained by matrix multiplication of proto and transposed mask outputs.

### Converted Model

Converted model outputs are the same as in 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:

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