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I tried to re-implement the GOOD results for voc-to-nonvoc setting and I am getting a lower performance. I trainied bot the stages from scratch following the instructions (i.e. generated depth and normal images, trained stage1 and stage 2). Here are the final results:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.041
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.064
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.040
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.026
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.051
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.073
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.173
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.243
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 30 ] = 0.284
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 50 ] = 0.336
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.388
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.448
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=500 ] = 0.469
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.220
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
As you can see, the AR@100 is 0.5% lower than what is reported in the paper. On comparing pseudoboxes that I get from stage 1 to the provided pseudoboxes, there are some differences. Is there a way to replicate stage-1 output?
Thanks!
Environment
Followed the same environment as given in the instructions.
Expected results
No response
Additional information
No response
The text was updated successfully, but these errors were encountered:
Prerequisite
💬 Describe the reimplementation questions
I tried to re-implement the GOOD results for voc-to-nonvoc setting and I am getting a lower performance. I trainied bot the stages from scratch following the instructions (i.e. generated depth and normal images, trained stage1 and stage 2). Here are the final results:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.041
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.064
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.040
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.026
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.051
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.073
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.173
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.243
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 30 ] = 0.284
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 50 ] = 0.336
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.388
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.448
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=500 ] = 0.469
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.220
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
As you can see, the AR@100 is 0.5% lower than what is reported in the paper. On comparing pseudoboxes that I get from stage 1 to the provided pseudoboxes, there are some differences. Is there a way to replicate stage-1 output?
Thanks!
Environment
Followed the same environment as given in the instructions.
Expected results
No response
Additional information
No response
The text was updated successfully, but these errors were encountered: