Closed LiewFeng closed 3 years ago
Verify that the xywh of the bounding boxes are in absolute coordinates, not relative coordinates. I.e., the x,y,w,h should be in number of pixels, not a proportion of image size.
I check the xywh in the predicted json. They are all in absolute coordinates, larger than 1.
Are you sure that your image ids match up with those found in the annotations file?
It seems matched. I conduct an experiment to veryfy this. For the same detection results, I set the img_id as a random int with the following code,
img_id = np.random.randint(1,len(self))
I run 5 times and get mAP as 0.19, 0.17, 0.25, 0.31, 0.19, which if lower than the mAP of original img_id as 5.7.
img_id = self.img_ids[idx]
Fixed. Exchanging the position of pred and gt works.
Hi, @dbolya , I'm interested in this work, but encountered a problem on Pascal VOC dataset, a very low mAP. 71.1 in the mmdetection v.s. 5.7 in tide. I tried to find the reason for several days, but failed. Could you kindly give some suggestions? Thanks a lot!
Code related to tide is the following,
gt = datasets.Pascal(path='pascal_test2007.json')
pred = datasets.COCOResult(path='pre.json.bbox.json')
tide = TIDE()
tide.evaluate_range(pred, gt, mode=TIDE.BOX )
tide.summarize()
I convert the detection results to COCO json style with the following code,![image](https://user-images.githubusercontent.com/42316773/118401695-375c9d00-b699-11eb-8475-3400d7915fa4.png)
The results are as follows,![image](https://user-images.githubusercontent.com/42316773/118401740-712da380-b699-11eb-92fd-bae475907b9b.png)