Comments (5)
Replacing multi-scale depthwise convolution with multi-scale attention mentioned in the original ICCV can improve the mAP from 43.7 => 44.4 with fewer FLOPs, faster training, and less GPU memory.
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We will update our ICCV submission with an explanation and results of the multi-scale bi-directional depthwise encoder.
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Encoder Atten=>DW
R50 43.7=>44.4
R101 44.4=>45.7
from smca-detr.
谢谢您的回复! 您的意思是您论文的结果是使用attn做的,但是使用depthwise conv可以达到更高的精度,占用更少的计算资源是吗?
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thanks for your interest in our paper.
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Related Issues (20)
- multiscale implementation HOT 2
- About input format HOT 20
- ulti_head_attention_forward gaussian[0].permute(2, 0, 1) RuntimeError: number of dims don't match in permute HOT 8
- IndexError: list index out of range when running inference HOT 10
- Question about the FLOPs in Table 1 HOT 3
- pretrained model download link HOT 6
- Does SMCA used warmup? HOT 1
- OOM for ResNet50-DC5 even for 32GB GPUs HOT 11
- 40.38 AP for ResNet50 single level feature HOT 2
- Pre-trained Model on VG
- ACT module release? HOT 1
- How to use target GPU for training?
- How to use target GPU for training? HOT 2
- Training coco metrics seems to be good but when trained model validated using --eval mode, only garbage values are printed. HOT 3
- Searching for help about the visiulization image. HOT 4
- ACT model compute flops HOT 5
- Code for calculating Flops
- An error occurred while using ACT
- 请问能否开源ACT论文中Knowlwdge Disillation Loss相关代码
- No module named 'ACT.extensions.ada_cluster'
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