regnetx-3.2gf

Use Case and High-Level Description

The regnetx-3.2gf model is one of the RegNetX design space models designed to perform image classification. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. This model was pretrained in PyTorch*. All RegNet classification models have been pretrained on the ImageNet* dataset. For details about this family of models, check out the Codebase for Image Classification Research.

Specification

Metric Value
Type Classification
GFLOPs 6.3893
MParams 15.2653
Source framework PyTorch*

Accuracy

Metric Original model Converted model
Top 1 78.15% 78.15%
Top 5 94.09% 94.09%

Input

Original model

Image, name - data, shape - 1,3,224,224, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Channel order is BGR. Mean values - [103.53,116.28,123.675], scale values - [57.375,57.12,58.395].

Converted model

Image, name - data, shape - 1,3,224,224, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Channel order is BGR

Output

Original model

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

• B - batch size
• C - Predicted probabilities for each class in [0, 1] range

Converted model

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

• B - batch size
• C - Predicted probabilities for each class in [0, 1] range

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 Converter:

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