mobilenet-v2-1.0-224.md

mobilenet-v2-1.0-224

Use Case and High-Level Description

mobilenet-v2-1.0-224 is one of MobileNet* models, which are small, low-latency, low-power, and parameterized to meet the resource constraints of a variety of use cases. They can be used for classification, detection, embeddings, and segmentation like other popular large-scale models. For details, see the paper.

Example

Specification

Metric	Value
Type	Classification
GFlops	0.615
MParams	3.489
Source framework	TensorFlow*

Performance

Input

Original Model

Image, name: input , shape: [1x224x224x3], format: [BxHxWxC], where:

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

Expected color order: RGB. Mean values: [127.5, 127.5, 127.5], scale factor for each channel: 127.5.

Converted Model

Image, name: input, shape: [1x3x224x224], format: [BxCxHxW], where:

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

Expected color order: BGR.

Output

Original Model

Name: MobilenetV2/Predictions/Reshape_1. Probabilities for all dataset classes (0 class is background). Probabilities are represented in logits format.

Converted Model

Name: MobilenetV2/Predictions/Softmax.
Probabilities for all dataset classes (0 class is background). Probabilities are represented in logits format.
Shape: [1,1001], format: [BxC], where:

- B - batch size
- C - vector of probabilities.

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TensorFlow.txt.