Salad Detector EfficientDet-Lite2 all classes coming back as 0.0

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