mobilenet-v2-1.4-224 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 the paper.
| Metric | Value |
|---|---|
| Type | Classification |
| GFlops | 1.183 |
| MParams | 6.087 |
| Source framework | TensorFlow* |
| Metric | Value |
|---|---|
| Top 1 | 74.09% |
| Top 5 | 91.97% |
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
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.
Probabilities for all dataset classes (0 class is background). Probabilities are represented in logits format. Name: MobilenetV1/Predictions/Reshape_1.
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.
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>
The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TF-Models.txt.