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Megvii-BaseDetection / DenseTeacher

DenseTeacher: Dense Pseudo-Label for Semi-supervised Object Detection
Apache License 2.0
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Cannot reproduce the results of the paper #36

Open kinredon opened 1 year ago

kinredon commented 1 year ago

Hi, thanks for your excellent work and the open-source.

I am trying to reproduce the results reported in the paper. Currently, I conduct the experiment of 1% COCO. I only obtained 18.5 AP, but the paper reported 19.64. Also, I use the large-scale jitter, setting the scale range to 400-1200. Still, I only obtained 20.7 AP (22.38 in the paper). 

|   AP   |  AP50  |  AP75  |  APs  |  APm   |  APl   |
|:------:|:------:|:------:|:-----:|:------:|:------:|
| 20.679 | 35.090 | 21.424 | 9.386 | 22.590 | 28.191 |

I train the model on 8 3090 GPUs, and I pick the highest AP model of the teacher model.

Here is the full log when I use the large-scale jitter log.txt.

ZRandomize commented 1 year ago

The result on 1% data seems unstable, please check the performance not only on the last checkpoint. Besides, check whether you are testing the student model, we reported the performance of teacher model in the paper.

Lyndon-wong commented 1 year ago

Maybe the weight of unsupervised loss should be set as 4 ? (as the Table 7(b) in the paper said)

PlutoQyl commented 11 months ago

Have you tried centerness branch？