Comments (2)
@hengshan123 maybe you can refer to ours where we implement the dense masked conv, without using MinkowskiEngine. Also the framework of SparK is more general as it can be used on any ConvNet without modifying ConvNet's structure like removing (LayerScale) or adding (global response norm) some layers.
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@shwoo93 I have the same issue. Do you plan to release the "dense masked convolution" approach for pretraining?
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Related Issues (20)
- unable to install apex HOT 2
- 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
- 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
- Pre-trained weights incompatible with backbone HOT 2
- deployment issue in trt fp16
- Onnx Export HOT 1
- [Question] Cosine Similarity
- Finetuning with limited Labels ?
- ImageNet 22k(21k) Traning loss at the end of training
- Why not provide 22k-supervised finetuning model??? I am really shocked by that every available ConvNeXt-V2 pre-training weights has been finetuned on imagenet-1k. Please make 22k-supervised ConvNeXt-V2 open just like ConvNeXt-V1 !!!!!! πππ HOT 2
- can grn add to resnet
- downsample_layer model order
- Colab Implementation
- GRN in paper uses x_i / sum x_j whereas code uses x_i / mean x_j
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