INT 8 quantization is not implemented when selected to export TensorFlow Lite

[franciscocostela]

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Bug

I trained an Object Detection Model using both YoloV5n and YoloV8n. In the Deploy tab, I selected TensorFlow Lite - advanced and selected 'Int 8 Quantization'. Then I clicked Export and downloaded the model when the button Download became available.

However, when I inspect the file, it looks like the quantization is never applied. This happens for both YoloV5n and YoloV8n

Is there anything that I am not doing correctly or is this really a bug?

Environment

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Minimal Reproducible Example

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[github-actions]

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[sergiuwaxmann]

@franciscocostela Hello!
Can you check if the exported model size is 4x smaller than the fp32 one?

[franciscocostela]

Hi Sergiu,

Yes - It is about 4x smaller. These are the sizes of the files:
Original FP32 - 11.6MB
Fp16 - 5.8MB
Int8 - 3.0MB

I am trying to run the TFLite file through a conversion pipeline to deploy it into a camera but it fails with an error message about the file not being quantized. When I inspect it with Netron, I see that the quantization bias is FLOAT32. INT8 is used in some of the convolution layers but not all of them (see screenshot). This seems to trigger the error message using the conversion pipeline.

[sergiuwaxmann]

@franciscocostela Based on the file size, the quantization is applied.

[github-actions]

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