Comments (3)
Thanks
It actually works on those long and thin objects, where 2 extreme points are physically close to each others
from dextr-pytorch.
Hello, thanks for the kind words. With 2 or 3 extreme points we are not able to recover the bounding box (in most cases). The bounding box leads to substantial performance improvements (Table 2, first row in the paper).
from dextr-pytorch.
As @kmaninis mentioned it is quite important for our method to use a bounding boxes. If you think you can obtain one from the 2 or 3 extreme points by assuming some parameters ie clicking the top and bottom and assuming always a fix width, then you could give it try!
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Related Issues (20)
- MS Coco Training Code HOT 1
- UR CODE WAS COPIED FOR SELL NOW HOT 1
- PASCAL Context Background (Stuff) Classes dataloader
- Results of dextr_pascal and dextr_coco models HOT 2
- Training time of PASCAL VOC 2012 HOT 1
- What if generated mask is not correct? HOT 1
- I can't click the extreme points in the picture HOT 1
- IndexError: index 0 is out of bounds for axis 0 with size 0 HOT 3
- RuntimeError: ONNX export failed: Couldn't export operator aten::adaptive_avg_pool2d
- Issue with running the demo file HOT 2
- Results on background classes of PASCAL Context HOT 1
- OSError: [WinError 126] The specified module could not be found HOT 1
- Speed metrics ? HOT 2
- How to train this model on other dataset? HOT 1
- process about clicks more than 4 points HOT 1
- demo.py blocks at line 49
- Question about Loss HOT 3
- List out of range HOT 2
- Train on Custom dataset in COCO style annotation
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