I viewed the 2 files in Netron.  They are exactly the same like:

detect2246×1760 268 KB

BUT when you print the details of the output tensorsyou have:
Your file:

[{‘name’: ‘StatefulPartitionedCall:3;StatefulPartitionedCall:2;StatefulPartitionedCall:1;StatefulPartitionedCall:02’, ‘index’: 600, ‘shape’: array([ 1, 25], dtype=int32), ‘shape_signature’: array([ 1, 25], dtype=int32), ‘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;StatefulPartitionedCall:2;StatefulPartitionedCall:1;StatefulPartitionedCall:0’, ‘index’: 598, ‘shape’: array([ 1, 25,  4], dtype=int32), ‘shape_signature’: array([ 1, 25,  4], dtype=int32), ‘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;StatefulPartitionedCall:2;StatefulPartitionedCall:1;StatefulPartitionedCall:03’, ‘index’: 601, ‘shape’: array([1], dtype=int32), ‘shape_signature’: array([1], dtype=int32), ‘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;StatefulPartitionedCall:2;StatefulPartitionedCall:1;StatefulPartitionedCall:01’, ‘index’: 599, ‘shape’: array([ 1, 25], dtype=int32), ‘shape_signature’: array([ 1, 25], dtype=int32), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

and old working file:

[{‘name’: ‘StatefulPartitionedCall:31’, ‘index’: 598, ‘shape’: array([ 1, 25,  4], dtype=int32), ‘shape_signature’: array([ 1, 25,  4], dtype=int32), ‘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:32’, ‘index’: 599, ‘shape’: array([ 1, 25], dtype=int32), ‘shape_signature’: array([ 1, 25], dtype=int32), ‘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:33’, ‘index’: 600, ‘shape’: array([ 1, 25], dtype=int32), ‘shape_signature’: array([ 1, 25], dtype=int32), ‘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:34’, ‘index’: 601, ‘shape’: array([1], dtype=int32), ‘shape_signature’: array([1], dtype=int32), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

I think the output arrays order has been changed. You can go here
  

      github.com
  

  
    tensorflow/examples/blob/master/lite/examples/object_detection/android/lib_interpreter/src/main/java/org/tensorflow/lite/examples/detection/tflite/TFLiteObjectDetectionAPIModel.java#L203-L206 23



      
      
          outputMap.put(0, outputLocations);
          outputMap.put(1, outputClasses);
          outputMap.put(2, outputScores);
          outputMap.put(3, numDetections);
      
    
  


  

  
    
    
  

  


and change the order to:

outputMap.put(0, outputScores);
outputMap.put(1, outputLocations);
outputMap.put(2, numDetections);
outputMap.put(3, outputClasses);

and I think your project will work again!
I do not know why this change happened but I feel like we have to tag @khanhlvg and @Yuqi_Li to shed some light or just to inform them.
If you need more help tag me.