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.
Metric | Value |
---|---|
Type | Classification |
GFlops | 0.028 |
MParams | 0.468 |
Source framework | TensorFlow* |
Metric | Value |
---|---|
Top 1 | 40.54% |
Top 5 | 65% |
Image, name: input
, shape: 1, 128, 128, 3
, format: B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channels
Expected color order: RGB
.
Mean values: [127.5, 127.5, 127.5], scale factor for each channel: 127.5
Image, name: input
, shape: 1, 128, 128, 3
, format: B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channels
Expected color order: BGR
.
Probabilities for all dataset classes in [0, 1] range (0 class is background). Name: MobilenetV1/Predictions/Reshape_1
.
Probabilities for all dataset classes in [0, 1] range (0 class is background). Name: MobilenetV1/Predictions/Softmax
, shape: 1, 1001
, format: B, C
, where:
B
- batch sizeC
- vector of probabilities.
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 Downloader:
omz_downloader --name <model_name>
An example of using the Model Converter:
omz_converter --name <model_name>
The original model is distributed under the
Apache License, Version 2.0.
A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0-TF-Models.txt
.