Comments (6)
@VikasRajashekar
The line#94 in config file also needs to be changed for one class, like following,
class_weight=[1.0] * 1 + [0.1])
Btw. The training step
and max_iters
(50e by default) need to be changed proportionally according to the number of your training images.
from boxinstseg.
@LiWentomng Thanks for the input.
I did change it. But however I face the following issue.
File "/netscratch/rajashekar/SAIL/BoxInst2/BoxInstSeg-main/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py", line 140, in instance_postprocess
File "/netscratch/rajashekar/SAIL/BoxInst2/BoxInstSeg-main/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py", line 140, in instance_postprocess
RuntimeError: selected index k out of range
scores_per_image, top_indices = scores.flatten(0, 1).topk(
RuntimeError: selected index k out of range
scores_per_image, top_indices = scores.flatten(0, 1).topk(
RuntimeError: selected index k out of range
scores_per_image, top_indices = scores.flatten(0, 1).topk(
I did debug the values of scores.flatten(0, 1),max_per_image,mask_cls in the file maskformer_fusion_head.
For each image it is always as follows:
scores.flatten(0, 1).shape=torch.Size([100])
max_per_image=1500
mask_cls=torch.Size([100, 2])
I tried to hardcode the topk to 100 and ran the evaluation but got very poor results:
COCOeval_opt.evaluate() finished in 12.34 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=2000 ] = 0.121
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=2000 ] = 0.239
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=2000 ] = 0.112
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=2000 ] = 0.082
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=2000 ] = 0.152
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=2000 ] = 0.249
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.165
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=500 ] = 0.165
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=2000 ] = 0.165
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=2000 ] = 0.096
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=2000 ] = 0.207
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=2000 ] = 0.377
Evaluating segm...
Loading and preparing results...
DONE (t=1.51s)
creating index...
index created!
Changing MaxDets and areas
Evaluate annotation type *segm*
COCOeval_opt.evaluate() finished in 18.51 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=2000 ] = 0.003
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=2000 ] = 0.006
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=2000 ] = 0.003
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=2000 ] = 0.003
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=2000 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=2000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=500 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=2000 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=2000 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=2000 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=2000 ] = 0.000
I am attaching the config file and the corresponding log file.
config_logs.zip
Am I missing something? Or is it a bug?
Looking forward for your inputs.
from boxinstseg.
@LiWentomng looking for your input.
from boxinstseg.
@LiWentomng Any update?
from boxinstseg.
@VikasRajashekar
Sorry to reply later! I'm busy recently so that with no input for this issue.
Have you test BoxInst
and BoxLevelset
in this rep for your dataset? Can it run well?
I have test the Box2mask on ICDAR2019 dataset with one class, which can run well without the above errors RuntimeError: selected index k out of range
.
I suggest you try the BoxInst
and BoxLevelset
firstly.
Any further questions can be discussed.
from boxinstseg.
I also observed the same trend. For Boxlevelset and box2mask, performance on single class is very poor. For Boxinst its decent.
@VikasRajashekar did you try BoxLevelset?
from boxinstseg.
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