Comments (5)
Hi @ShoufaChen @huilicici , firstly, thanks to the authors for their good work.
Actually, I have the same confusion. DDIM conducts MSE loss between Gaussian noise and the output of the denoiser (U-Net) during the training stage. However, in DiffusionDet, it seems that the denoiser (cascade decoder) is directly optimized to refine the noisy box to obtain ground truth boxes, which works very differently from the conventional DDIM.
I am not sure whether it can be seen as introducing a denoising task like DN-Detr. Based on this understanding, the sampling steps in the inference stage also should not have an observable influence on the detection performance.
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Hi,
The set prediction loss contains one item
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As presented in Algorithm 1 Training of DDPM(https://arxiv.org/pdf/2006.11239v2.pdf), in step 5, a gradient descent step is adopted to constrain the diffused output to be Gaussian. Does DiffusionDet need such kind of loss to contrain the corrupt bboxes to be Gaussian?
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Also have the same confusion. Waiting for the answer
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I have same question as @gugite
Can you please respond to this? @ShoufaChen It will be very helpful.
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Related Issues (20)
- ap=0
- why the dataset only split to train and test? HOT 2
- Detectron's version of cuda
- Questions on box renewal
- 使用提供的训练模型做eval只有32mAP
- train on my datasets,adjust the value of thread
- 复现问题
- RuntimeError: nvrtc: error: invalid value for --gpu-architecture (-arch) ON RTX4090
- random seed 为什么固定了随机种子和参数后,模型的预测框测试出来都一样
- coco dataset
- pre-trained models
- AP is nan
- evaluator
- Used Multi gpus but takes same time with 1 gpu training
- bbox=0
- Too many boxes when using demo.py with --video-input video.mp4
- why the eval result is 0?
- 为什么推理结果为0?
- ddim中对于time生成的程序语句似乎不对?
- Prepare datasets HOT 1
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