Closed guoqiang0148666 closed 4 years ago
General VOC setting is train on voc0712 and test on voc07:
if is_train:
image_set = ("voc2007_trainval", "voc2012_trainval")
else:
image_set = ("voc2007_test", )
And we focus on AP@0.5 in VOC experiments.
Thanks
Thank you for your excellent works!
l am freshman to simpledet. l follow bellow commands to train and test faster rcnn on pascal voc 2007 dataset. l think these procedures are ok, but l got very low mAP , l don't know what's went wrong, l hope someone can help me, any suggestion will be welcomed!
Thanks!
create pascal voc roidb
They create _trainval.roidb and _test.roidb in data/cache dir, l rename them to pascal_voc_train2007.roidb and pascal_voc_val2007.roidb
create label_map file
train and test faster rcnn
my config file is modifed from 'config/faster_r50v1c4_c5_512roi_1x.py', the different is as follow :
@@ -77,7 +78,7 @@ def get_config(is_train): class BboxParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer
@@ -96,9 +97,9 @@ def get_config(is_train):
image_set = ("pascal_voc_val2007", )
backbone = Backbone(BackboneParam) neck = Neck(NeckParam) @@ -168,7 +169,7 @@ def get_config(is_train): thr = 0.5
annotation = None
data processing
class NormParam:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.625 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.244 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.423 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.434 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.164 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.497 coco eval uses: 13.6