Comments (2)
we can use the everything mode as demonstrated here, which input uniform sampled points on the images as prompt.
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if I'm not mistaken, you need the prompt encoder to determine embeddings on an image to mask. automask generator actually is a bit misleading as it just generates a point prompt every 20 pixels or so. For each point prompt, embeddings are encoded and these are matched with the model on their IOU and the most probable (or top 3) is determined to be the masks.
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Related Issues (20)
- How can I test the model HOT 1
- The purpose of self.embedding_maskfeature HOT 3
- About the number of detect boxes HOT 2
- Can I have a more specific using example (code snippet) in README.md? HOT 5
- Question about Ablation study HOT 4
- RuntimeError: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW
- Can we add support for transformers? HOT 3
- RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 1 but got size 13 for tensor number 1 in the list. HOT 1
- Training problem: During the training of sam-hq, the iou output of the val set is very high, 0.98; but in eval mode, the iou of the val set is only 0.48
- Segment Anything CPP Wrapper for macOS
- Can sam-hq perform breakpoint training: continue training on the already trained sam-hq, or continue training on the official sam-hq weights HOT 2
- Alternative implementation in Refiners
- About interm_embeddings
- Runtime error in Colab HOT 1
- How to train vit_tiny (Light HQ-SAM for real-time need): ViT-Tiny HQ-SAM model?
- Why can't we achieve the demonstration effect? There are three tennis rackets in the incoming image, and only one mask is returned
- Visualization of Figure 6
- can't we get candidates as original sam?
- About the issue of positive and negative sample pairs HOT 1
- Does SAM_HQ support batch input?
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