Comments (2)
czczup
commented on May 29, 2024
Hi, your notebook is very good. All the steps are correct.
This output is just a warning, not an error. This warning is due to the use of LayerScale in our model, which was not included in the original DeiT pre-trained weights.
The output of image_demo.py is saved as a file in demo/. You can check if this file is generated.
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LongPML
commented on May 29, 2024
Thanks for your response.
I currently want to use ViT-Adapter for real time driving scene sematic segmentation for about 10 classes. Would you recommend some suitable models for tuning?
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Related Issues (20)
- knowledge distillation HOT 1
- About the GFlops on the ADE20K dataset. HOT 3
- swin-t-adapter HOT 1
- Cannot download the checkpoint of upernet_deit_adapter_tiny.
- After Freezing the Vit backbone how many iterations are supposed to be trained?
- How can I use other pretrained ViTs or CNNs?
- number of iteration in the case of a batchsize equals to 1
- May I ask how to export onnx? There has been an error HOT 1
- ViT Adapter Not Working With Patch Size Different From 16 HOT 1
- Relative log amplitudes of Fourier transformed feature maps.
- Reccomended config for the case of binary segmentation
- KeyError: 'ViTAdapter is not in the models registry' HOT 2
- Poor performance on custom dataset.
- Dimension mismatching for beit_dapter with changing img_size and crop_size from (512, 512) to (384, 384)
- LayerScale and pre-trained models HOT 5
- Can ViT-Adapter be exported as an ONNX model? What are the respective versions of PyTorch and the ONNX subset for this?
- Request the configuration file of objects365 pre-training
- 从 mmseg0.x 迁移至 mmseg 1.x 复现出现问题
- 关于segmentation和detection的BEiT实现略有不同的问题 HOT 2
- Missing pretrained weights HOT 1
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