Comments (7)
I'm sorry for this bug. I have fixed it. But it doesn't have obvious impact on our result since it's unlikely for our method to find 0 relevant code in the database similar to a query.
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Hi, I have one more thing to discuss with you. The relevant_num for calculating average precision in your code is how many similar images in the query result. But from my understanding, the relevan_num is the number that how many similar images should be in the query result (retrieval all similar images). Take the imageNet experiment as an example, the relevant_num should be fixed to 1000
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In our code, the relevant_num is how many similar images within the closest 1000 images to the query image. MAP 1000 means that we retrieve the closest 1000 images and calculate the MAP.
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Think about this: If only the top 2 retrieval images are similar, the relevant_number= 2, then the average precision equals to 100%. But there are more than 1000 similar images in your database. The AP should be calculated as (1/1+2/2)/1000 = 0.2%. check this discussion
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AP is a ranking metric. If the top 2 retrievals in the ranked list are relevant (and only the top 2), AP is 100%. You're talking about Recall, which in this case is indeed 0.2%.
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@kunhe. Actually, I'm not pretty sure about this. Have you checked the above discussion? I think when we calculate the MAP, the recall should guarantee to equal to 100%.
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@akturtle If you want 100% recall, you should go down the ranked list until all relevant items are found, say at position K. Sure, you can compute MAP on the sublist from 1 to K, knowing that recall is 100%. But here MAP is computed by fixing K=1000, and 100% recall may or may not happen at that point.
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Related Issues (20)
- Problem with MAP@k
- 你好,非常感谢分享的NUS数据集,但是下载解压发现数据的图片比原始要少 HOT 2
- Why take two batches as input HOT 4
- the mAP on cifar10 is only 0.30 HOT 5
- the scale tanh is useless HOT 1
- a question about training set HOT 1
- The mAP of ImageNet is different from that on paper HOT 7
- How did you generate the "train.txt" of MS-COCO? HOT 1
- The pretrained model HOT 1
- nuswide dataset HOT 1
- @bfan @caozhangjie HOT 1
- question on nuswide dataset
- Pytorch with Resnet50 HOT 6
- HashNet on CUB200 HOT 1
- Anybody willing to share a pretrained model (from ImageNet or CoCo or similar)?
- AlexNet backbone result (COCO) HOT 3
- False sampling of data HOT 1
- test.py for CIFAR HOT 1
- number of training images for imagenet HOT 1
- question on nuswide81 dataset HOT 1
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