nfnet-f0¶

Use Case and High-Level Description¶

NFNet F0 is one of the image classification Normalizer-Free models pre-trained on the ImageNet dataset. NFNets are Normalizer-Free ResNets in which use Adaptive Gradient Clipping (AGC), which clips gradients based on the unit-wise ratio of gradient norms to parameter norms.

F0 variant is the baseline variant with a depth pattern [1, 2, 6, 3] (indicating how many bottleneck blocks to allocate to each stage). Each subsequent variant has this depth pattern multiplied by N (where N = 1 for F0).

The model input is a blob that consists of a single image of 1, 3, 256, 256 in RGB order.

The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.

For details see repository and paper.

Specification¶

Metric	Value
Type	Classification
GFLOPs	24.8053
MParams	71.4444
Source framework	PyTorch*

Accuracy¶

Metric	Value
Top 1	83.34%
Top 5	96.56%

Input¶

Original model¶

Image, name - image, shape - 1, 3, 256, 256, format is B, C, H, W, where:

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

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

Converted model¶

Image, name - image, shape - 1, 3, 256, 256, 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 - probs, shape - 1, 1000, output data format is B, C, where:

B - batch size
C - predicted probabilities for each class in logits format

Converted model¶

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

B - batch size
C - predicted probabilities for each class 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:

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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