# 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.

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

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>