Comments (3)
I would recommend following the steps listed in the README which describe this exact scenario.
You do not need a GPU to run the tool, only to get the image embeddings. So I would suggest copying your images to the server, extracting your embeddings on the server, and compiling the onnx models there (using the helpers I provided, copy them to your server).
Next, copy the images, embeddings and models locally. Install SALT locally. The onnx model is fast enough for interactive use on a CPU.
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Thanks for your reply. I have tried this way after submitting this issues. This works on my laptop. I had considered that CPU is not fast enough before submitting this issues, and it is totally wrong. Thanks for your tools again, i would like to use it and make some contribution to this project if possible.
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The SAM paper mentions that their decoder (the ONNX model) was shipped to their annotators, who I assume wouldn't have very fast GPUs locally. Also their online demo runs the decoder on your browser. I just wrote a tool to do the same thing that their demo does. :)
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Related Issues (20)
- Failed to run, maybe caused by parsing annotations.json HOT 2
- TypeError: 'torch._C.Value' object is not iterable HOT 1
- remote labeling HOT 1
- Package libffi conflict
- Pink line generated from the top left corner of the image HOT 2
- Excluded regions are still getting annotated within the annotated area of the image.
- IndexError: list index out of range
- How to do box labeling mode? I left clicked just marked the mask HOT 1
- Error for low quality image HOT 2
- An error occurred when importing the labeling results into cvat, has anyone encountered it?
- When you add it, you get this extra piece. How to solve HOT 1
- ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (8,) + inhomogeneous part.
- Suggestion - Integrate MobileSAM into the pipeline for lightweight and faster inference HOT 1
- Error when clicking middle mouse button
- There is a problem with the reshape node exported by ONNX. I checked the network structure and found that it is only a simple reshape node. Have you encountered this problem
- check this problem, has been quite persistent, when creating a label which is partially covered under a label. Crashing Frequntly HOT 1
- Unreasonable segmentation results HOT 1
- A bug occur when I want to change the categories
- Support for adding custom model? HOT 3
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