mobilenet-v1-0.50-160.md

mobilenet-v1-0.50-160

Use Case and High-Level Description

mobilenet-v1-0.50-160 is one of MobileNets - 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. For details, see paper.

Example

Specification

Metric	Value
Type	Classification
GFlops	0.156
MParams	1.327
Source framework	TensorFlow*

Performance

Input

Original Model

Image, name: input, shape: [1x160x160x3], 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: [1x3x160x160], format: [BxCxHxW], where:

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

Expected color order: BGR.

Output

Original Model

Probabilities for all dataset classes (0 class is background). Probabilities are represented in logits format. Name: MobilenetV1/Predictions/Reshape_1.

Converted Model

Probabilities for all dataset classes (0 class is background). Probabilities are represented in logits format. Name: MobilenetV1/Predictions/Softmax, shape: [1,1001], format: [BxC], where:

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

Legal Information

https://raw.githubusercontent.com/tensorflow/models/master/LICENSE