Inputmeta File Modification

Crucial step before quantization

After importing model successfully, the <model>_inputmeta.yml will be generated. You need to modify it properly according to your model’s feature. So you need to understand the features of your custom model, such as the input shape, input format, data normalization etc. Below are the parameters that need to be modified for different models:

YOLOv4-tiny

Parameter	Value
reverse_channel	false
scale	0.003921568627451
add_preproc_node	true
preproc_type	IMAGE_RGB888_PLANAR

Note

If you retrain your model with a different dataset, please update the mean/scale accordingly as the mean/scale depends on the feature of dataset and data normalization.

YOLOv7-tiny for Pytorch version

Tip

Kindly convert your model .pt format to .onnx format before importing.

Parameter	Value
reverse_channel	false
scale	0.003921568627451
add_preproc_node	true
preproc_type	IMAGE_RGB888_PLANAR

Note

If you retrain your model with a different dataset, please update the mean/scale accordingly as the mean/scale depends on the feature of dataset and data normalization.

MobileFaceNet

Parameter	Value
reverse_channel	false
mean	123.675, 116.28, 103.53
scale	0.003921568627451
add_preproc_node	true
preproc_type	IMAGE_RGB888_PLANAR

Note

If you retrain your model with a different dataset, please update the mean/scale accordingly as the mean/scale depends on the feature of dataset and data normalization.

SCRFD

Parameter	Value
reverse_channel	false
mean	127.5, 127.5, 127.5
scale	0.0078125
add_preproc_node	true
preproc_type	IMAGE_RGB888_PLANAR

Note

If you retrain your model with a different dataset, please update the mean/scale accordingly as the mean/scale depends on the feature of dataset and data normalization.

YAMNet

This model cannot be quantized due to the use of STFT/FFT operator. You may proceed straight to export step.

CNN Model for Image classification

Tip

For Tensorflow keras .h5 models, please do not save optimizer data in .h5 file. Otherwise it will fail when starting to import H5 model. To ignore optimizer data, please add include_optimizer=False in command. For example: model.save(‘model.h5’, include_optimizer=False)

RGB

Parameter	Value
reverse_channel	false
scale	0.003921568627451
add_preproc_node	true
preproc_type	IMAGE_RGB888_PLANAR

Gray

Some models which require gray image dataset, for example, fer2013_cnn, you need to modify yml file differently as the Acuity tool doesn’t support bmp files.

Parameter	Value
reverse_channel	false
scale	0.003921568627451
add_preproc_node	true
preproc_type	TENSOR

Note

TENSOR means the data will be native tensor data without any header. For gray image dataset, tensor files instead of jpeg or bmp files are used for calibration.