I’ve been working on training the EfficientDet-Lite2 model to recognize my own object. Almost everything works, the new model outlines my object just fine however my classes always come back as zero. Which means that I can’t tell the difference between my object and something else the model recognizes (such as a person).
Here’s the output of the classes, scores, boxes and count when I run inference on colab. In this case I have a picture of a person, and a picture of my object and it outlines both of them cleanly. But nothing is returned in the classes. What am I missing?
edit: I tried again with the latest release of salad detector and got the same results. If I just run the example as is, I get classes out along with boxes and scores.  But after I train it with my data instead, I get boxes for my and other objects, but no classes.

0.3
CLASSES
{‘name’: ‘StatefulPartitionedCall:2’, ‘index’: 783, ‘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’: {}}
<class ‘numpy.ndarray’>
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.]
END_CLASSES
SCORES
{‘name’: ‘StatefulPartitionedCall:1’, ‘index’: 784, ‘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’: {}}
<class ‘numpy.ndarray’>
[0.95703125 0.8046875  0.171875   0.13671875 0.1328125  0.109375
0.10546875 0.0703125  0.0703125  0.06640625 0.0625     0.05078125
0.05078125 0.046875   0.04296875 0.04296875 0.04296875 0.04296875
0.0390625  0.0390625  0.0390625  0.0390625  0.0390625  0.03515625
0.03515625]
END_SCORES
BOXES
{‘name’: ‘StatefulPartitionedCall:3’, ‘index’: 782, ‘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’: {}}
<class ‘numpy.ndarray’>
[[ 0.10341597  0.07837614  0.29824328  0.2694588 ]
[ 0.0022909   0.35881817  1.0972939   0.92067206]
[ 0.36508366  0.52892387  1.1009921   0.9214685 ]
[ 0.1687806   0.46745497  0.89054465  0.8574684 ]
[ 0.4743509   0.43261927  1.0083578   0.84755   ]
[-0.10687697  0.48249638  0.8727542   1.1378279 ]
[ 0.42514202  0.2712517   1.2569318   0.93022966]
[ 0.01708302  0.06039434  0.26142305  0.28192842]
[-0.26614624  0.2336869   0.8273681   0.9557977 ]
[ 0.09977013  0.38245487  0.5424307   0.8367195 ]
[ 0.10326806  0.13250032  1.0232235   0.78903913]
[ 0.8101959   0.61326325  0.85606337  0.6559801 ]
[ 0.85945106  0.5897225   0.9021679   0.63271666]
[ 0.19703382  0.6065323   0.8779749   0.9492126 ]
[ 0.70410407  0.46735543  0.7527213   0.51062864]
[ 0.8126633   0.5612771   0.85428905  0.6029028 ]
[ 0.40730965  0.27877745  0.9373851   0.5131608 ]
[ 0.20224822  0.5947402   1.1581546   1.156594  ]
[ 0.77688324  0.5397553   0.82071996  0.58247226]
[ 0.8606483   0.64405364  0.9065157   0.6876077 ]
[ 0.8977856   0.6129542   0.9421932   0.6535166 ]
[ 0.10990259  0.10467488  0.28687546  0.20217824]
[ 0.15204903  0.15080991  0.3211866   0.25086924]
[ 0.75420994  0.488369    0.8028272   0.5336468 ]
[ 0.82664245  0.7167883   0.87047917  0.7611959 ]]
END_BOXES
COUNT
{‘name’: ‘StatefulPartitionedCall:0’, ‘index’: 785, ‘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’: {}}
<class ‘numpy.ndarray’>
25.0
END COUNT