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*

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 [0, 1] range

### 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 [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.