How to convert CustomVision.ai TensorFlow/TensorFlow Lite model into something that works with TFLite Object Detection API?

Question

We have a model exported from CustomVision.ai (it supports exporting TensorFlow .pb, TensorFlow Lite .tflite, Saved Model .pp formats).

We would like to integrate this model into an existing app that uses the TFLite Object Detection API, which expects these inputs and outputs:

--input_arrays=normalized_input_image_tensor \
--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \

The model seems to have very different inputs and outputs:

Inputs array: Placeholder
name: Placeholder
type: float32[1,416,416,3]
quantization: 0 ≤ q ≤ 255
location: 0

Outputs array: model_outputs
name: model_outputs
type: float32[1,13,13,45]
location: 44

If I run the "tflite_convert" command

tflite_convert \
    --graph_def_file=model.pb \
    --output_file=detect.tflite \
    --input_shapes=1,416,416,3 \
    --input_arrays=normalized_input_image_tensor \
    --output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \
    --inference_type=FLOAT  \
    --allow_custom_ops

I get:

ValueError: Invalid tensors 'normalized_input_image_tensor' were found.

Any idea how to get this to work? I've been digging around all day and have only come up dry...any help would be appreciated! Thanks!

Answer 1

Not all models are compatible with TensorFlow Lite Object Detection API.

The above model you have does not fit with the TensorFlow Lite object detection compatible.

For example, input signature requires one image tensor and output signature requires four tensors to be taken by the TensorFlow Lite Object Detection API.

Please consider using the TensorFlow Lite Model Maker or TF Model Garden's Object Detector API to generate a compatible model.