Comments (1)
Dear @Gasethata ,
The approach you want to use is not the purpose of this repository. Here I developed metrics for object detections for different classes using bounding boxes.
It seems to me that your evaluation method is pixel-wise instead of using bounding boxes. You should try to look for metrics that are applied to semantic segmentation approach, and for the most popular metrics used in your particular case. But if you still want to use bounding boxes to evaluate the detected classes' regions, you can adapt my code to obtain bounding boxes of the pixel regions. If you do it for ground truth and detected images, you can use my code to obtain mAP. But again, I don't know if this is suited to your case.
Regards.
from object-detection-metrics.
Related Issues (20)
- Is this a BUG? HOT 2
- How to calculate precision of small, medium and large objects ? HOT 3
- need to cast classId to str when saving PlotPrecisionRecallCurve HOT 2
- Question about the argument -imgsize HOT 1
- Confidence threshold HOT 8
- NMS ?? HOT 3
- Possible bug in BoundingBox.py? HOT 1
- How to calculate mAP@[0.5:0.99]? HOT 2
- red boxes represent detections with prediction label of that class or any detections whenever it prediction is that class ? HOT 2
- incorrect equation HOT 4
- ggggg
- Check performance of the trained model HOT 2
- The way to set which object is TP when more than one detection overlapping a ground truth seems to be wrong HOT 2
- How can i get TP,TN,FP,FN from it? HOT 1
- [question] support for 3d volumes? HOT 1
- Difference implementations between this repo and the faster_rcnn ones HOT 1
- What are the dimensions of precision represent? HOT 1
- Got `AP=0.00%` when running `pascalvoc.py` with samples
- image3 G iou ? HOT 2
- getting threshold values of confidence score which recall/precision calculated HOT 1
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from object-detection-metrics.