Hello,
i recently trained a ssd_resnet model for some custom classification.
In general everything worked out as i was able to run inference on the data which was produced by the training process.
What i want to do is to convert the model to a tflite model and compile it for a coral TPU.
What does not work atm: I cannot find any information on how to convert my model into a tflite model.
The training process was done using the script model_main_tf2.py from the object detection API.
This produces files as follows:

checkpoint
ckpt-XXX.data-00000-of-00001
ckpt-XXX.index
(which is as far as i can tell everything)

I am aware that there are a lot of sites talking about converting modesl to tflite but none that work from the kind of checkpoints i have. (it always refers to model.ckpt files with some kind of meta file given which i guess is a tf1 format?)
What i tried:


using the export scripts provided by the opject detection API (export_tflide_ssd_graph.py, export_tflite_graph_tf2.py) - both did not work as it says: “your model is not built”.


loading and building the model and then manually exporting it:
# load model
configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)
model_config = configs['model']
detection_model = model_builder.build(model_config=model_config, is_training=False)

# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-63')).expect_partial()
detection_model.build((614,514))
converter = tf.lite.TFLiteConverter.from_keras_model(detection_model)
tflite_model = converter.convert()```



which results in the same problem telling me that the input is not set and that i could solve this by “building” the model. (which i thought i did…?)
So if anyone can shed some light on how this could work or where i can find any documentation on how to convert these checkpoints to a .tflite model i’d be really grateful. Let me know if i forgot to provide any information.
Cheers.