我在 pytorch 上实现了一个更快的 RCNN 网络。我已按照下一个教程进行操作。
https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html
在一些图像中,我有 100 多个对象要分类。但是,在本教程中,我最多只能检测 100 个对象,因为参数“maxdets”= 100。
有没有办法改变这个值以适应我的项目?
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.655
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.105
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.006
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.066
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
如果只改变下一个参数,问题就解决了吗?
cocoeval.Params.setDetParams.maxDets = [1, 10, 100]
谢谢!