Comments (2)
- Failed to solve it with lower down-sampling rates
- We decreased down-sampling rates to 2, 4, 8, 16 and 32
- Refer to this commit for more details
- It achieves lower MAE on DUTS-TE (0.0463 to 0.0452) at 48 epochs
Figure 03. Inference results of DDRNet23-slim with lower down-sampliong rates for Figure 1<\sup>
- However, it still creates diamond patterns
- I.e. We cannot effectively solve it with lower down-sampling rates
- Then, what about the values of down-sampling rate in PoolNet and CSFNet?
Figure 04. Inference results of PoolNet-ResNet50-wo-edge with lower down-sampliong rates for Figure 1<\sup>
- PoolNet-ResNet50-wo-edge down-samples the spatial size of input image by factor of 2 in the output layer (
ScoreLayer
)
Figure 05. Inference results of CSFNet-Res2Net50 with lower down-sampliong rates for Figure 1<\sup>
- CSFNet-Res2Net50 down-samples the spatial size of input image by factor of 4 in the output layer (
self.cls_layer
) - It seems CSFNet-Res2Net50 also suffers from the problem
- lowering down-sampling rates may improve some of it
- However, it requires tremendous FLOPS and time complexity
from ddrnet.
- Refer to Pixelation for more details
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Related Issues (12)
- Inference feature for random data HOT 1
- Train DDRNet on multiple famous public datasets for salient object detection
- Train
- Request a feature for training DDRNet-23-slim on datasets for salient object detection like DUTS HOT 1
- RuntimeError: The size of tensor a (38) must match the size of tensor b (37) at non-singleton dimension 2 HOT 1
- Increase the resolution of training images in DUTS-TR without scaling
- ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 512, 1, 1]) HOT 2
- RuntimeError: Error(s) in loading state_dict for DualResNet: ... HOT 1
- Modify the architecture of DAPPM considering the spatial size of the feature maps from generated by DAPPM HOT 3
- Transfer learning for salient object detection HOT 4
- Future work for transfer learning on salient object detection HOT 2
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