Fix the num_classes issue.

num_classes should be 1 + actual_classes.
Related to google#492  google#391  google#521

efficientdet/README.md

@@ -262,7 +262,7 @@ Download data and checkpoints.
 
 Create a config file for the PASCAL VOC dataset called voc_config.yaml and put this in it.
 
-      num_classes: 20
+      num_classes: 21
       var_freeze_expr: '(efficientnet|fpn_cells|resample_p6)'
       label_id_mapping: {0: background, 1: aeroplane, 2: bicycle, 3: bird, 4: boat, 5: bottle, 6: bus, 7: car, 8: cat, 9: chair, 10: cow, 11: diningtable, 12: dog, 13: horse, 14: motorbike, 15: person, 16: pottedplant, 17: sheep, 18: sofa, 19: train, 20: tvmonitor}
 
@@ -295,7 +295,7 @@ Install [horovod](https://github.com/horovod/horovod#id6).
 
 Create a config file for the PASCAL VOC dataset called voc_config.yaml and put this in it.
 
-      num_classes: 20
+      num_classes: 21
       var_freeze_expr: '(efficientnet|fpn_cells|resample_p6)'
       label_id_mapping: {0: background, 1: aeroplane, 2: bicycle, 3: bird, 4: boat, 5: bottle, 6: bus, 7: car, 8: cat, 9: chair, 10: cow, 11: diningtable, 12: dog, 13: horse, 14: motorbike, 15: person, 16: pottedplant, 17: sheep, 18: sofa, 19: train, 20: tvmonitor}
 

efficientdet/hparams_config.py

@@ -189,7 +189,9 @@ def default_detection_configs():
   h.augmix_params = (3, -1, 1)
 
   # dataset specific parameters
-  h.num_classes = 90
+  # TODO(tanmingxing): update this to be 91 for COCO, and 21 for pascal.
+  h.num_classes = 90  # 1+ actual classes, 0 is reserved for background.
+
   h.skip_crowd_during_training = True
   h.label_id_mapping = None
   h.max_instances_per_image = 100  # Default to 100 for COCO.
@@ -246,7 +248,7 @@ def default_detection_configs():
   h.conv_bn_act_pattern = False
 
   # For post-processing nms, must be a dict.
-  h.nms_configs = None
+  h.nms_configs = {'method': 'hard'}
 
   # version.
   h.fpn_name = None