Cannot train retinanet with a single class
See original GitHub issueDescribe the bug
I cannot get any results from training retinanet with 1 class.
Log doesn’t contain anything after 2020-02-26 14:50:59,227 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
, however gpu seems loaded.
Reproduction
- What command or script did you run? colab notebook config on gist
!python tools/train.py "{home}/retinanet_fp16.py"
!python tools/test.py "{home}/retinanet_fp16.py" {checkpoint_file} --eval mAP
- Did you make any modifications on the code or config? Did you understand what you have modified?
I set
num_classes=2,
, yes I understand. - What dataset did you use? I am using my custom dataset with about 70 images and bboxes.
I pickled annotations, here’s the first line:
{'ann': {'bboxes': array([[ 25.373676, 293.4756 , 190.30257 , 336.04767 ]], dtype=float32),
'labels': array([0])},
'filename': '000001-2020-02-14T16:50:17.554Z.jpg',
'height': 399,
'width': 599}
Environment Google Colab with GPU
Issue Analytics
- State:
- Created 4 years ago
- Comments:6
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Top GitHub Comments
Hi @manycoding MMDet v2.0 has been released. You may set
num_classes=1
in v2.0. Subset training is also supported in #2340 .@xvjiarui How to label a single category with labelme and instance segmentation? There are cat, cat, cat, cat0, cat1, cat2 or cat-0, cat-1, cat-2, etc. which annotation method is used?thank you!
I use cat-0, cat-1, cat-2 for annotation, and use labelme2coco to convert annotations.json, where the annotation is [], what’s the matter?