efficientnet-v2-s#

Use Case and High-Level Description#

The efficientnet-v2-s model is a small variant of the EfficientNetV2 pre-trained on ImageNet-21k dataset and fine-tuned on ImageNet-1k for image classification task. EfficientNetV2 is a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. A combination of training-aware neural architecture search and scaling were used in the development to jointly optimize training speed and parameter efficiency.

More details provided in the paper and repository.

Specification#

Metric

Value

Type

Classification

GFlops

16.9406

MParams

21.3816

Source framework

PyTorch*

Accuracy#

Metric

Value

Top 1

84.29%

Top 5

97.26%

Input#

Original Model#

Image, name: input, shape: 1, 3, 384, 384, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: RGB. Mean values - [127.5, 127.5, 127.5], scale values - [127.5, 127.5, 127.5].

Converted Model#

Image, name: input, shape: 1, 3, 384, 384, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Output#

Original Model#

Object classifier according to ImageNet classes, name: logits, shape: 1, 1000, output data format is B, C, where:

  • B - batch size

  • C - vector of probabilities for all dataset classes in logits format

Converted Model#

Object classifier according to ImageNet classes, name: logits, shape: 1, 1000, output data format is B, C, where:

  • B - batch size

  • C - vector of probabilities for all dataset classes in logits format

Download a Model and Convert it into OpenVINO™ IR Format#

You can download models and if necessary convert them into OpenVINO™ IR 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>

Demo usage#

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: