Comments (2)
The proposed FCMAE is not limited to a specific convolutional model. While we have not tried it on other architectures, it will likely provide a meaningful initialization typically.
In contrast, the GRN is initially designed to correct the feature collapsing issue observed in the original ConvNeXt model after the FCMAE training. The impact of GRN on other architectures is to be studied.
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hi,have you try any other convnet,like resnet ,mobilenet?any tips for me ?
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Related Issues (20)
- The official pre-training weights does not match the parameters of the model HOT 1
- The reconstruction images have a lot of noise HOT 5
- segmentation
- Unable to update submodule (publickey error) HOT 4
- visualization and feature consine distance HOT 2
- unable to install apex HOT 2
- On masking input images HOT 1
- Make image and patch sizes dynamic
- Adapting ConvNetV2 for Time Series: Inconsistencies between Pre-training Visuals and Fine-Tuning Performance HOT 1
- could you share the dense masked conv based sparse encoding ? HOT 2
- ImageNet-1K pre-trained weights ConvNeXt V2 supervised HOT 1
- GRN can be used on any convent with FCMAE ?anyone tried this ?
- Cannot install MinkowskiEngine with provided instructions HOT 2
- Weights trained using pre-training script produce NaN values when running fine-tuning script
- 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
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