mobilenet-v1-0.25-128

Use Case and High-Level Description

mobilenet-v1-0.25-128 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 0.028
MParams 0.468
Source framework TensorFlow*

Accuracy

Metric Value
Top 1 40.54%
Top 5 65%

Performance

Input

Original Model

Image, name: input , shape: [1x128x128x3], format: [BxHxWxC], where:

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: [1x3x128x128], format: [BxCxHxW], where:

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:

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.