What are net-scale-factor and offset of restnet-10 trained with TLT3.0?

• Hardware Platform (Jetson / GPU):Both
• DeepStream Version:5.1
• JetPack Version (valid for Jetson only):4.5
• TensorRT Version:7.1.3

config file:
classifier_1.txt (436 Bytes)

Hi,
I trained resnet-10 classification model on my own dateset, and has 2 classes.

Q1: Doing inference the trained model with tlt evaluation and enable-center-crop=True, I get high accuracy, and I want to know how I can set enable-center-crop=True in the config file? Is it necessary? I test the trained model with tlt evaluation with enable-center-crop=False, I get worse results.

Q2: In the config file, there are net-scale-factor, offset and model-color-format, What are these values?

Q3- The input size of model is 224x224x3, Is it necessary to resize all of images to 244x224x3 before training?

No. Currently deepstream does not support it.

There are detailed description of the parameters in Gst-nvinfer — DeepStream 5.1 Release documentation

Yes.

So I have to train the model with enable-center-crop=False?

Seems yes.

In such way the evaluation accuracy is dropped 2-3%.

@Fiona.Chen,
I know offset is the means of each RGB channels, But in practical, because I used fine-tune on my own dataset, The values of offset mode should to be in documentation or config training, I don’t know I have to calculate offset values for my own dataset?

offset is used in normalization.

@Fiona.Chen, Is it possible to refer to normalization?

What do you mean?

There is detailed description of “offset” in Gst-nvinfer — DeepStream 5.1 Release documentation

enable-center-crop=False only impact evaluation, it will not impact training. We do not need to implement center crop with inference processor.

enable-center-crop=False only impact evaluation, it will not impact training. We do not need to implement center crop with inference processor.

When we train model with center-crop, It’s better to use center-crop in the inference mode.

If enable-center-crop=False impact in evaluation, So it definitely impact in the inference set If we don’t set center-crop=True.

The training is using random crop.

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