Comments (3)
thanks for the contribution . I trained the 224 x 224 imageNet classification model, while the acc has a gap between mine and yours. Hope there could be the pretrained model and related setting. Thanks.
Hello,can you tell me what is the result of your training model?How much is the gap with the paper?Thank you.
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@Mollylulu Thanks for your concerns, the pretrained weights are committed. I was trying to train the models in a smaller batch-size and observed a bit of performance drop (81.8 vs 82.0) in a single node (8x V100 or 3090 GPU) with 1024 batch-size or less. In fact, the results in the paper are trained under a batch size of 4096 with 32 A100 GPU for faster iterations of the experiments. Could you report your training setup?
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It should be the batch_size and gpu version, sorry for the late reply.
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Related Issues (20)
- how to visualize Figure 5,Figure 6, Figure7 HOT 3
- The computational cost of deformabel attention HOT 2
- some questions about the reference points and offset network HOT 1
- what's the difference between DAT and deformable DETR's attention? HOT 3
- Changing model input size from 384 -> 1024 HOT 2
- About Low Accuracy HOT 1
- Deformable Attention Journal Paper not referenced
- 训练时计算维度出错 HOT 1
- Which version of imagenet are you using? Is it ILSVRC 2012? HOT 1
- Face negative dimension issue when running on CIFAR10 HOT 1
- evaluate.sh: line 6: path-to-imagenet: No such file or directory HOT 1
- Controlling the number of keys per query HOT 1
- an unused parameters for class TransformerStage:"ns_per_pt" and "sr_ratio" HOT 1
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- 您好,DAT可以当作即插即用的模块吗 HOT 1
- How to run the model HOT 1
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- 处理文本数据或一维序列数据 HOT 1
- 采样点个数问题
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