After running  training  for 100 epochs, all IOU results are 0.0 and -1.0 which seems quite strange. I’ve got a learning rate = 0.005, momentum = 0.5 and weight_decay = 0.005, which, after reading some posts related to such results (AP = 0.0) might solve such results.  Could anything in the model definition be leading to these results? Any comments will be appreciated.
Test:  [0/1]  eta: 0:00:27  model_time: 26.7939 (26.7939)  evaluator_time: 0.0214 (0.0214)  time: 27.9693  data: 1.1538  max mem: 0
Test: Total time: 0:00:28 (28.1102 s / it)
Averaged stats: model_time: 26.7939 (26.7939)  evaluator_time: 0.0214 (0.0214)
Accumulating evaluation results...
DONE (t=0.06s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000