George_Soloupis:

. So you have to do the same procedure there also eg. preprocess the image, make the predictions with the Interpreter, post process and create the boxes on the image.
If indeed this is an Object detection project check a good example here:


Hi @George_Soloupis  The objective of doing the above colab notebook is to do face recognition by retraining the existing models( in this case i took ssd_mobilenet_v2_320x320_coco17_tpu-8 after testing for models like SSD MobileNet v2 320 x 320 and EffiecientDet D0 512 x 512 )
I have created a notebook where i am doing correct inference with the images in Colab.
Now my effort was to create a model.tflite file, which initially i was unable to do. After further query i came across that the mechanism of creating tflite is like running
!python /content/gdrive/MyDrive/TFLite_Check/models/research/object_detection/export_tflite_graph_tf2.py 
–pipeline_config_path {pipeline_file} 
–trained_checkpoint_dir {last_model_path} 
–output_directory {outd}
followed by
converter = tf.lite.TFLiteConverter.from_saved_model(’/content/gdrive/MyDrive/TFLite_Check/freezetflite/saved_model/’)
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.allow_custom_ops=True
I implemented these commands in Colab notebook which i shared earlier, not in the one where i am doing the inferences as they appear in the notebook
I was trying to check if the output of the interpreter call returns an image as i had seen in the colab notebook Selfie2Anime maintained by @Sayak_Paul
This is why i was calling plt.imshow(output()[0])
So how do i go ahead now…
I am trying to understand the notebook link you have shared