Comments (3)
Hi I sometimes meet the same problem. I guess the reason is that the MMD selection process can be optimized. During this process, the utilization of GPU is somewhat low. I run Cityscpes to Foggy at around 1 or 1.5 days for convergence with one V100 and around 150-200 epochs. May this help you :)
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Thanks for the reply!
I have one more question.
I ran the code using command given in the main page, which is python train.py --img 640 --batch 12 --epochs 300 --data ./data/city_and_foggy8_3.yaml --cfg ./models/yolov5x.yaml --hyp ./data/hyp_aug/mm1.yaml --weights '' --name "test"
But I got mAP50 value of 0.3482 (averaged over two experiments, 0.3498 and 0.3466 each).
Did I miss something?
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Hi can you share your training log? And does the few-shot target dataset contain 8 images?
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Related Issues (15)
- missing train_MMD and loss HOT 2
- Question: stop gradient HOT 4
- cannot acces the dataset HOT 5
- minimum gpu memory requird and runtime error while running YOLOs, YOLOm, YOLOl HOT 2
- How to select images for the few shot settings in cityscapes_foggy dataset HOT 1
- there are always the same issue,when i run train.py in different gpu HOT 17
- without train_MMD.py HOT 18
- Performance on cityscapes to foggy cityscapes HOT 4
- val data HOT 1
- The MMD_distance in train.py is not used HOT 1
- code for extracting object level features HOT 1
- error in MMD_distance, The size of tensor a (20) must match the size of tensor b (256) at non-singleton dimension 3 HOT 2
- 代码配置中的layer4,layer6,layer9是什么意思? HOT 5
- env HOT 1
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