Comments (4)
Hi,
The current version only contains unparalleled CASD_IW(54.6%). Paralleled CASD_LW and regression_brand is coming soon.
from casd.
Hello, your work is excellent!
But the result of your code is only 53.5% mAP, which is significantly lower than 56.8% mAP reported in your paper.
Any suggestions to how one might reproduce the published results?the classification AP is AP for aeroplane = 0.9836 AP for bicycle = 0.9709 AP for bird = 0.9765 AP for boat = 0.9553 AP for bottle = 0.8009 AP for bus = 0.9501 AP for car = 0.9724 AP for cat = 0.9677 AP for chair = 0.7514 AP for cow = 0.9229 AP for diningtable = 0.8283 AP for dog = 0.9770 AP for horse = 0.9687 AP for motorbike = 0.9549 AP for person = 0.9897 AP for pottedplant = 0.8265 AP for sheep = 0.9278 AP for sofa = 0.8116 AP for train = 0.9726 AP for tvmonitor = 0.9102 __________________ the mAP is 92.0957 Evaluating detections Writing aeroplane VOC results file Writing bicycle VOC results file Writing bird VOC results file Writing boat VOC results file Writing bottle VOC results file Writing bus VOC results file Writing car VOC results file Writing cat VOC results file Writing chair VOC results file Writing cow VOC results file Writing diningtable VOC results file Writing dog VOC results file Writing horse VOC results file Writing motorbike VOC results file Writing person VOC results file Writing pottedplant VOC results file Writing sheep VOC results file Writing sofa VOC results file Writing train VOC results file Writing tvmonitor VOC results file VOC07 metric? Yes AP for aeroplane = 0.6933 AP for bicycle = 0.6773 AP for bird = 0.5468 AP for boat = 0.3945 AP for bottle = 0.2312 AP for bus = 0.6964 AP for car = 0.6922 AP for cat = 0.7217 AP for chair = 0.2173 AP for cow = 0.6577 AP for diningtable = 0.4632 AP for dog = 0.6464 AP for horse = 0.5931 AP for motorbike = 0.6789 AP for person = 0.1639 AP for pottedplant = 0.2502 AP for sheep = 0.5352 AP for sofa = 0.5645 AP for train = 0.6312 AP for tvmonitor = 0.6608 Mean AP = 0.5358
Hi, when I run the codes, there occurred an error: 'Selective search data not found at: data/selective_search_data/voc_2007_trainval.mat' in pascal_voc.py , did you meet the same problem?
from casd.
Hello, your work is excellent!
But the result of your code is only 53.5% mAP, which is significantly lower than 56.8% mAP reported in your paper.
Any suggestions to how one might reproduce the published results?the classification AP is AP for aeroplane = 0.9836 AP for bicycle = 0.9709 AP for bird = 0.9765 AP for boat = 0.9553 AP for bottle = 0.8009 AP for bus = 0.9501 AP for car = 0.9724 AP for cat = 0.9677 AP for chair = 0.7514 AP for cow = 0.9229 AP for diningtable = 0.8283 AP for dog = 0.9770 AP for horse = 0.9687 AP for motorbike = 0.9549 AP for person = 0.9897 AP for pottedplant = 0.8265 AP for sheep = 0.9278 AP for sofa = 0.8116 AP for train = 0.9726 AP for tvmonitor = 0.9102 __________________ the mAP is 92.0957 Evaluating detections Writing aeroplane VOC results file Writing bicycle VOC results file Writing bird VOC results file Writing boat VOC results file Writing bottle VOC results file Writing bus VOC results file Writing car VOC results file Writing cat VOC results file Writing chair VOC results file Writing cow VOC results file Writing diningtable VOC results file Writing dog VOC results file Writing horse VOC results file Writing motorbike VOC results file Writing person VOC results file Writing pottedplant VOC results file Writing sheep VOC results file Writing sofa VOC results file Writing train VOC results file Writing tvmonitor VOC results file VOC07 metric? Yes AP for aeroplane = 0.6933 AP for bicycle = 0.6773 AP for bird = 0.5468 AP for boat = 0.3945 AP for bottle = 0.2312 AP for bus = 0.6964 AP for car = 0.6922 AP for cat = 0.7217 AP for chair = 0.2173 AP for cow = 0.6577 AP for diningtable = 0.4632 AP for dog = 0.6464 AP for horse = 0.5931 AP for motorbike = 0.6789 AP for person = 0.1639 AP for pottedplant = 0.2502 AP for sheep = 0.5352 AP for sofa = 0.5645 AP for train = 0.6312 AP for tvmonitor = 0.6608 Mean AP = 0.5358
hello, i want to ask you a question. when I run the codes, there occurred an error: 'Selective search data not found at: data/selective_search_data/voc_2007_trainval.mat', I want to know where or how to get this file?
from casd.
Hi,
The current version only contains unparalleled CASD_IW(54.6%). Paralleled CASD_LW and regression_brand is coming soon.
Hi,
It's been a while since you said paralleled version will be added but I don't think it has been updated yet. Do you have a plan to add it sometime soon?
from casd.
Related Issues (20)
- About the implementation for bbr reg head. HOT 1
- Selective search data not found at: /sequoia/data2/vavo/wsod/CASD/data/selective_search_data/voc_2007_trainval.mat HOT 3
- cu:259
- Question about cls_det_loss & when will the code supporting parallelism be released.
- Question about consistency loss!!! HOT 4
- Hi, I want to know why not set that self.ca_iw is true in the begining. Looking forward to your reply. Thank you very much!
- Issue with CUDA11.1
- Hi my bro. When will the code supporting parallelism be released ? And where is the layer-wise loss in code?Thank you! HOT 7
- Question about the attention map in the paper HOT 1
- Correction in README.md
- Hi, my bro. Why layer-wise CASD is still not added into the project CASD-may30 ? Thank you.
- Why is self.ca_iw initialized to False in network.py of the project CASD-may 30? Thank you!
- CUDA out of memory
- Usability of BDD100K dataset
- THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=383 error=11 : invalid argument
- ValueError: unsupported pickle protocol: 5 HOT 1
- ImportError: cannot import name 'roi_pool_cuda' from 'ops.roi_pool'
- Can't find the layer-wise CASD code in both master and may30
- importerror
- ImportError: cannot import name 'nms_cpu' from partially initialized module 'lib.ops.nms' (most likely due to a circular import) (F:\多目标弱监督\CASD-master\lib\ops\nms\__init__.py)
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