mobilenet-v1-1.0-224

.0-224_mobilenet-v1-1.0-224

## Use Case and High-Level Description

mobilenet-v1-1.0-224 is one of MobileNet V1 architecture with the width multiplier 1.0 and resolution 224. It is small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models are used.

## Specification

Metric Value
Type Classification
GFlops 1.148
MParams 4.221
Source framework Caffe*

Metric Value
Top 1 69.496%
Top 5 89.224%

## Input

### Original model

Image, name - input , shape - 1,3,224,224, format B,C,H,W, where:

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


Expected color order: BGR. Mean values - [103.94,116.78,123.68], scale factor for each channel - 58.8235294117647

### Converted model

Image, name - input , shape - 1,3,224,224, 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

Object classifier according to ImageNet classes, name - prob, shape - 1,1000, output data format is B,C where:

• B - batch size
• C - Predicted probabilities for each class in [0, 1] range

### Converted model

Object classifier according to ImageNet classes, name - prob, shape - 1,1000, output data format is B,C where:

• B - batch size
• C - Predicted probabilities for each class in [0, 1] range

## Legal Information

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