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(Need Delete) [PaddlePaddle Hackathon] Task 66 #4342
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* update link to release/2.2
* clean code of s2anet * fix for paddle==2.1.0 * remove debug info * update comment
* add kitti metric * clean kitti metric code * fix kitticars doc * fix feature model cfgs * add kitti metric
…Paddle#3844) * [dev] rbox update2 (PaddlePaddle#3828) * set lr for 4 card as default, and update * fix error
* add frame_rate for mot infer video * add doc of frame_rate for mot video infer, test=document_fix
* updata document, test=document_fix
* udpate verison require & fix typo
…addle#4207) * fix operators typo * fix centernet_head bias init
…lePaddle#4097) * modify VOCDataSet and default value of allow_empty * revert default value of allow_empty (PaddlePaddle#4150) Co-authored-by: wangguanzhong <jerrywgz@126.com>
* fix operators typo * fix kitti metric deploy
…ork for single gt box)
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PR这个分支与develop分支不一致 |
PR types
New features
PR changes
APIs
Describe
你好,
该PR根据 #4225 添加了多尺度测试
使用方法
在配置文件中添加MultiscaleTestResize配置,具体使用方法可以参考新添加的配置文件:configs/faster_rcnn/faster_rcnn_r34_fpn_multiscaletest_1x_coco.yml
测试方法
评估
(该测试使用了backbone为resnet34的faster rcnn配置,需要修改上面的权重文件为正确位置)
预测
评估效果对比
根据上面的配置文件的配置(resnet34-faster-rcnn-fpn)在coco2017验证集上进行评估
多尺度
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.588
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.416
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.413
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.512
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.320
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.504
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.530
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.334
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.564
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.680
[10/15 13:50:21] ppdet.engine INFO: Total sample number: 4952, averge FPS: 5.121671015456265
单尺度
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.378
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.586
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.412
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.219
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.410
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.314
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.496
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.521
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.333
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.646
[10/15 14:07:58] ppdet.engine INFO: Total sample number: 4952, averge FPS: 15.107217863848637