Closed 9friday closed 2 years ago
Hi @9friday, Follow the instructions mentioned on the tutorial https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/eager_few_shot_od_training_tflite.ipynb. Thank you!
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Closing as stale. Please reopen if you'd like to work on this further.
System information
Describe the problem
I am working on object detection application on android using the TensorflowLite C++API. When I convert ssd_mobilenet_v2_coco_2018_03_29 model to tflite, output of converted tflite model differs from output of originally provided .pb file on the same test images.
Converting the .pb file to .tflite
Terminal Command(I used this link as example.):
python3 object_detection/export_tflite_ssd_graph.py --pipeline_config_path=/content/ssd_mobilenet_v2_coco_2018_03_29/pipeline.config --trained_checkpoint_prefix=/content/ssd_mobilenet_v2_coco_2018_03_29/model.ckpt --output_directory=/tmp/tflite_graph --add_postprocessing_op=true
The above command threw an error:
google.protobuf.text_format.ParseError: 109:7 : Message type "object_detection.protos.SsdFeatureExtractor" has no field named "batch_norm_trainable"
The error was resolved after following the steps suggested here.
But, the detection output of the converted tflite model differs from that of the originaly provided .pb file.
Output Images:
Using .pb (and .pbtxt) files:
Using converted .tflite file:
Any help and guidance would be appreciated. Cheers:)