Comments (4)
Generally, it may need some more memory if for image instance segmentation. If you want to reduce the memory, maybe you can try use 2x upsampling about the basis mask.
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@JialeCao001 I see.Thanks for your reply!I noticed that you used 8GPUs during training.Could you tell me what the memory size of per GPU?
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I remember about maybe 20G per GPU. If we limit the number of proposals for mask prediction per GPU, it maybe less than 20G.
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I remember about maybe 20G per GPU. If we limit the number of proposals for mask prediction per GPU, it maybe less than 20G.
Thank you very much for your prompt reply!It really helps me a lot.
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Related Issues (20)
- How to visualize the results? HOT 3
- crop_split_cuda? HOT 1
- 关于SipMask-VIS的预训练模型 HOT 4
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- SipMask-VIS tracking implementation HOT 9
- About the testing process
- SP module implementation location
- SipMask-VIS mAP is not stable HOT 2
- python setup.py develop ERROR HOT 2
- How do I set my custom label map? HOT 3
- Licensing issue
- Annotation format for a huge Dataset
- how to measure the fps? HOT 1
- AttributeError: 'float' object has no attribute 'detach' HOT 1
- 您好!我把网络改了一点点之后,训练是收敛的,但是为什么精度都是0呢?
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- What is the problem : crop_split_cuda_backward(): incompatible function arguments.
- Hello, sorry to bother you. I wonder if you are still maintaining this code. I would like to ask what gt_pids, gt_INds and idx_GT stand for in anchor_head of SIPmask_VIS
- only size-1 arrays can be converted to python scalars HOT 2
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