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chengchunhsu / EveryPixelMatters

Implementation of ECCV 2020 paper "Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector"
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Fail to get the same results #6

Open neineimeow opened 3 years ago

neineimeow commented 3 years ago

Hi ! Thanks for your wonderful work ~But i fail to get as well as results in your paper. Can you post the ga results with VGG16 backbone in cityscape->foggy cityscape ? GA: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.174 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.157 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.156 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.052 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.262 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.560

GA_CA: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.183 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.340 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.050 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.582

mochaojie commented 3 years ago

Hi, i have received the cityscapes dataset. However, when i click the link for foggy cityscapes, i don't know which one is i really need. In the website, i see some foggy cityscapes datasets for semantic segmentation, not for object detection. Can you give me the link for foggy dataset specifically. Thanks!

neineimeow commented 3 years ago

Foggy cityscapes need u email to their team. You can see it on cityscape homepage

tmp12316 commented 3 years ago

Hi ! Thanks for your wonderful work ~But i fail to get as well as results in your paper. Can you post the ga results with VGG16 backbone in cityscape->foggy cityscape ? GA: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.174 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.157 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.156 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.052 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.262 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.560

GA_CA: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.183 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.340 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.050 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.582

Hi, are these the best AP50 results selected in your experiments? I find that the results are sensitive to the training iteration. If I choose some inappropriate iteration to evaluate, the result will be very bad. How about yours? Thanks