Comments (2)
The first mask is to ensure that the pixels dropped by the mask will not affect the convolution, and the second mask is to ensure that the part of the mask is still 0 after conv
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@powermano thank you for the answer. Could you explain how the input pixels could affect the convolution if we didn't apply the first mask?
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Related Issues (20)
- On masking input images HOT 1
- Make image and patch sizes dynamic HOT 1
- Adapting ConvNetV2 for Time Series: Inconsistencies between Pre-training Visuals and Fine-Tuning Performance HOT 1
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- ImageNet-1K pre-trained weights ConvNeXt V2 supervised HOT 1
- GRN can be used on any convent with FCMAE οΌanyone tried this ?
- Cannot install MinkowskiEngine with provided instructions HOT 2
- Weights trained using pre-training script produce NaN values when running fine-tuning script HOT 3
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