Comments (3)
ZhengPeng7
commented on August 25, 2024
It could be not too much degradation in performance. Those with deform_conv2d should work a little better, but I don't know how much it is. However, I still personally suggest keeping the deform_conv2d. If you have the computation resources, you can have a try on training it.
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JunseokLee3
commented on August 25, 2024
Thank you for the response. I will give it a try
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ZhengPeng7
commented on August 25, 2024
Hi, you can now find the ONNX files of all weights files in the GitHub release or the ONNX folder in the stuff folder on my GDrive. For more info, plz check the ONNX conversion part in the model zoo section. You can find the inference codes in notebooks there.
Tell me if you encounter any further problems when using them :)
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Related Issues (20)
- CoreML model HOT 5
- Question: training time for different models HOT 6
- Model name correspondence HOT 6
- Custom dataset fine-tuning HOT 4
- Feature: Onnx Export HOT 8
- Model weights when trained on HQ-Seg44k dataset HOT 1
- training datset set HOT 2
- Releated to finetunning HOT 5
- KeyError: 'HOME' HOT 1
- I made local Windows, cloud RunPod, Massed Compute and free Kaggle and more advanced Gradio APP with batch inference and transparent background HOT 2
- Model Zoo - general use is matting or segment? HOT 4
- inquiry about the convneck in ViTMatte HOT 3
- So many models HOT 11
- Onnx accuracy HOT 6
- dont work on folder , dont work on image sets and if i download it is original image -> i mus t push my right mousebuttun to save it HOT 8
- 各数据集上分别使用的最佳backbone HOT 1
- Adapting Model Training for Custom Image Dimensions (200x200): Seeking Guidance and Solutions HOT 2
- Heavy and Portrait models HOT 5
- Availability of FAL's versions of the model (heavy/light/portrait) HOT 1
- 使用optimum-cli导出为ONNX报错 HOT 1
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