I am trying to run tflite model on android device and another working model has below output:

[{‘name’: ‘serving_default_input_2:0’, ‘index’: 0, ‘shape’: array([ 1, 384, 384, 3]), ‘shape_signature’: array([ -1, 384, 384, 3]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

[{‘name’: ‘StatefulPartitionedCall:1’, ‘index’: 245, ‘shape’: array([ 1, 16]), ‘shape_signature’: array([-1, 16]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:0’, ‘index’: 238, ‘shape’: array([ 1, 100]), ‘shape_signature’: array([ -1, 100]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

{‘serving_default’: {‘inputs’: [‘input_2’], ‘outputs’: [‘classifier’, ‘locator’]}}

but my data has the below and it has a lot differency:

[{‘name’: ‘serving_default_images:0’, ‘index’: 0, ‘shape’: array([ 1, 320, 320, 3]), ‘shape_signature’: array([ 1, 320, 320, 3]), ‘dtype’: <class ‘numpy.uint8’>, ‘quantization’: (0.0078125, 127), ‘quantization_parameters’: {‘scales’: array([0.0078125], dtype=float32), ‘zero_points’: array([127]), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

[{‘name’: ‘StatefulPartitionedCall:1’, ‘index’: 600, ‘shape’: array([ 1, 25]), ‘shape_signature’: array([ 1, 25]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:3’, ‘index’: 598, ‘shape’: array([ 1, 25, 4]), ‘shape_signature’: array([ 1, 25, 4]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:0’, ‘index’: 601, ‘shape’: array([1]), ‘shape_signature’: array([1]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}, {‘name’: ‘StatefulPartitionedCall:2’, ‘index’: 599, ‘shape’: array([ 1, 25]), ‘shape_signature’: array([ 1, 25]), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

{‘serving_default’: {‘inputs’: [‘images’], ‘outputs’: [‘output_0’, ‘output_1’, ‘output_2’, ‘output_3’]}}

As I understood I need to have a model with different parameters. Could anyone help at this point?