Comments (3)
Dear @ydixon ,
I have not tried to reproduce the YOLO v3 results published on the paper. The IOU used is 50% as in the PASCAL VOC metric. But it is necessary to check on the paper the NMS used.
If I am not mistaken, YOLO v3 page might have the trained weights to reproduce the results.
Regards
from object-detection-metrics.
@rafaelpadilla Thanks for getting back to me.
I've written a few simple python scripts to convert results generated from AlexeyAB/darknet to formats usable by both pycocotools and this repo. Interestingly, the mAP values from both repo are very close sitting at 0.530 and 53.4%. So I'm guessing the problem lye within somewhere else. I'll let you know if I find out more.
`
AP: 73.43% (aeroplane)
AP: 15.97% (apple)
AP: 31.12% (backpack)
AP: 31.64% (banana)
AP: 51.75% (baseball_bat)
AP: 49.85% (baseball_glove)
AP: 78.70% (bear)
AP: 69.22% (bed)
AP: 33.26% (bench)
AP: 50.93% (bicycle)
AP: 46.00% (bird)
AP: 47.34% (boat)
AP: 16.65% (book)
AP: 43.80% (bottle)
AP: 48.90% (bowl)
AP: 30.81% (broccoli)
AP: 81.79% (bus)
AP: 48.09% (cake)
AP: 60.59% (car)
AP: 22.00% (carrot)
AP: 77.35% (cat)
AP: 42.31% (cell_phone)
AP: 42.51% (chair)
AP: 73.79% (clock)
AP: 44.61% (cow)
AP: 49.41% (cup)
AP: 45.60% (diningtable)
AP: 78.24% (dog)
AP: 40.49% (donut)
AP: 79.89% (elephant)
AP: 80.86% (fire_hydrant)
AP: 37.61% (fork)
AP: 73.14% (frisbee)
AP: 86.74% (giraffe)
AP: 9.09% (hair_drier)
AP: 21.43% (handbag)
AP: 77.39% (horse)
AP: 40.34% (hot_dog)
AP: 68.82% (keyboard)
AP: 43.40% (kite)
AP: 29.95% (knife)
AP: 71.92% (laptop)
AP: 69.98% (microwave)
AP: 66.37% (motorbike)
AP: 70.41% (mouse)
AP: 32.29% (orange)
AP: 49.43% (oven)
AP: 54.77% (parking_meter)
AP: 71.93% (person)
AP: 58.52% (pizza)
AP: 43.61% (pottedplant)
AP: 72.45% (refrigerator)
AP: 49.44% (remote)
AP: 45.99% (sandwich)
AP: 38.44% (scissors)
AP: 56.43% (sheep)
AP: 60.97% (sink)
AP: 69.94% (skateboard)
AP: 37.55% (skis)
AP: 48.45% (snowboard)
AP: 56.02% (sofa)
AP: 24.63% (spoon)
AP: 62.04% (sports_ball)
AP: 78.23% (stop_sign)
AP: 48.00% (suitcase)
AP: 64.26% (surfboard)
AP: 58.62% (teddy_bear)
AP: 71.64% (tennis_racket)
AP: 52.70% (tie)
AP: 19.82% (toaster)
AP: 75.81% (toilet)
AP: 34.84% (toothbrush)
AP: 51.36% (traffic_light)
AP: 81.07% (train)
AP: 48.85% (truck)
AP: 75.88% (tvmonitor)
AP: 57.70% (umbrella)
AP: 49.23% (vase)
AP: 49.24% (wine_glass)
AP: 79.87% (zebra)
mAP: 53.54%`
from object-detection-metrics.
Dear @ydixon ,
I know what you mean. This little difference among the official tools (like COCO, VOC PASCAL) and 3rd party projects that motivated me to create our project. After understanding exactly how the official codes implement the metrics, I was able to reproduce exactly their results.
I have been busy with other projects and have not published the COCO metrics. Meanwhile, I suggest you to use the official COCO implementation to report your results. Once I publish the COCO metrics, I can guarantee that our results will be identical to the official metrics.
Best regards
from object-detection-metrics.
Related Issues (20)
- 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 3
- I think a bug resulting in incorrect calculation for Average Precision HOT 1
- Definition of xywh is <left> <top> <width> <height> or <center-x> <center-y> <width> <height>
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from object-detection-metrics.