Comments (6)
We release Container+Single-scale SMCA which can achieve 44.2-44.4 mAP compared with ResNet50+Scale-scale SMCA which achieve 41.0mAP.
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https://drive.google.com/file/d/1dFkr1QAbBpX2Gss0nl4TQ--URUTK7TZ0/view?usp=sharing
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@gaopengcuhk thank u. How's the speed compare with r50?
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On Imagenet classification(224), Container is comparable with R50 on FLOPs and speed with much better performance. On Object detection which requires 1333 large resolution, Container is slower than R50. I do not benchmark the speed.
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@gaopengcuhk why container is slower in large input ?
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All Transformer based models like PVT, Container, SWIN is slower than ResNet 50 on large image.
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Related Issues (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
- 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
- 关于多尺度SMCA的代码 HOT 5
- Question about use multi-GPU for training HOT 2
- what's the modification compare with original DETR HOT 4
- multiscale implementation HOT 2
- About input format HOT 20
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