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.
Specification¶
Metric |
Value |
---|---|
Type |
Classification |
GFlops |
0.615 |
MParams |
3.489 |
Source framework |
TensorFlow* |
Accuracy¶
Metric |
Value |
---|---|
Top 1 |
71.85% |
Top 5 |
90.69% |
Input¶
Original Model¶
Image, name: input
, shape: 1, 224, 224, 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.
Converted Model¶
Image, name: input
, shape: 1, 3, 224, 224
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- 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: B, C
, where:
B
- batch sizeC
- vector of probabilities.
Download a Model and Convert it into Inference Engine Format¶
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:
python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
An example of using the Model Converter:
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
Legal Information¶
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
.