Comments (4)
Additional resources for fine-tuning
- https://encord.com/blog/learn-how-to-fine-tune-the-segment-anything-model-sam/
- https://github.com/ziqi-jin/finetune-anything
- https://github.com/luca-medeiros/lightning-sam
- https://github.com/tianrun-chen/SAM-Adapter-PyTorch
- facebookresearch/segment-anything#5
- https://colab.research.google.com/drive/1F6uRommb3GswcRlPZWpkAQRMVNdVH7Ww?usp=sharing
from segment-geospatial.
https://github.com/bowang-lab/MedSAM
from segment-geospatial.
so cool,bro!
from segment-geospatial.
For now, we can only fine-tune what SAM allows. See the parameters below. It might take sometime before we can support other model fine-tuning. Contributions are always welcome.
https://github.com/opengeos/segment-geospatial/blob/main/samgeo/samgeo.py#L43
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
from segment-geospatial.
Related Issues (20)
- Mask from automatic mask generator with batch mode not working
- samgeo.hq_sam not working in docker HOT 4
- Add SAM-based object detection using selecting just one sample of a class HOT 2
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- batch predict
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- warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE Could not solve for environment specs The following package could not be installed └─ segment-geospatial is installable and it requires └─ pycocotools with the potential options HOT 5
- SamGeo() cannot download model vit_h (samgeo.hq_sam) HOT 7
- Expose leafmap backend options to user HOT 7
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from segment-geospatial.