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:

Classification Python* Demo

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-PyTorch-Image-Models.txt.

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