Comments (1)
We hold the view that the closer to the prediction layer, the richer the classification and localization information. In our method, the features extracted by the N consecutive conv layers are used to predict both the classification and localization directly. Therefore, the features extracted by the N consecutive conv layers have richer classification and localization information for task-interaction, than the feature extracted by the previous backbone+FPN.
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Related Issues (20)
- If I want to load the resnet50-19c8e357.pth from my local path instead of downloading from the terminal,how can I change the code? HOT 2
- Error during training (Assertion input_val >= zero && input_val <= one failed.) HOT 6
- Support for Swin backbone
- Plot result HOT 10
- i changed the number of ratios, then model can not train ,where should i have to modify futher?
- How about the ATSS assigner as initial static assignment method? HOT 2
- RuntimeError
- the image size HOT 1
- Layer Attention instead of Channel Attention? HOT 5
- Tood's onnx file request HOT 3
- Benchmark / FPS
- N个连续的卷积层 HOT 8
- 是否可以提供没有使用tap的tal训练config?
- TOOD
- Welcome update to OpenMMLab 2.0
- 请问TAL中,t的计算公式是否为:t = (s**alpha) * (u**beta),t = t/t.max * u.max? HOT 2
- Can I ask some questions about TOOD/mmdet/core/bbox/assigners/task_aligned_assigner.py ? HOT 4
- RuntimeError HOT 3
- Can I use it in two-stage method?
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