# aclnet-int8¶

## Use Case and High-Level Description¶

The AclNet-int8 model is quantized and fine-tuned with NNCF variant of AclNet model, which is designed to perform sound classification. The AclNet-int8 model is trained on an internal dataset of environmental sounds for 53 different classes, listed in file <omz_dir>/data/dataset_classes/aclnet_53cl.txt. For details about the model, see this paper.

The model input is a segment of PCM audio samples in N, C, 1, L format.

The model output for AclNet-int8 is the sound classifier output for the 53 different environmental sound classes from the internal sound database.

Metric

Value

Type

Classification

GFLOPs

2.71

MParams

1.41

Source framework

PyTorch*

## Accuracy¶

Metric

Value

Top 1

87.1%

Top 5

93.0%

Metrics were computed on internal validation dataset according to following publication and paper.

## Input¶

### Original Model¶

Audio, name - result.1, shape - 1, 1, 1, L, format is N, C, 1, L, where:

• N - batch size

• C - channel

• L - number of PCM samples (minimum value is 16000)

### Converted Model¶

Audio, name - result.1, shape - 1, 1, 1, L, format is N, C, 1, L, where:

• N - batch size

• C - channel

• L - number of PCM samples (minimum value is 16000)

## Output¶

### Original Model¶

Sound classifier (see labels file, <omz_dir>/data/dataset_classes/aclnet_53cl.txt), name - 486, shape - 1, 53, output data format is N, C, where:

• N - batch size

• C - predicted softmax scores for each class in [0, 1] range

### Converted Model¶

Sound classifier (see labels file, <omz_dir>/data/dataset_classes/aclnet_53cl.txt), name - 486, shape - 1, 53, output data format is N, C, where:

• N - batch size

• C - predicted softmax scores 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>