Image Segmentation
Transformers
PyTorch
pixdlm
cvpr-2026
compute-transparency
reasoning-segmentation
uav
remote-sensing
vision-language
Instructions to use WhynotHug/PixDLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WhynotHug/PixDLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="WhynotHug/PixDLM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WhynotHug/PixDLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,208 Bytes
3334467 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | #!/usr/bin/env python3
import argparse
from pathlib import Path
def ensure_link(link: Path, target: str):
if link.exists() or link.is_symlink():
return
try:
link.symlink_to(target)
except OSError:
print(f"Could not create symlink {link} -> {target}. Please create it manually.")
def main():
parser = argparse.ArgumentParser(description="Prepare DRSeg compatibility links.")
parser.add_argument("--data-root", default="data/DRSeg")
args = parser.parse_args()
root = Path(args.data_root)
if not root.exists():
raise SystemExit(f"Missing data root: {root}")
ensure_link(root / "CODrone", ".")
if (root / "label").exists():
ensure_link(root / "labels", "label")
required = [
root / "DRtrain",
root / "DRval",
root / "DRtest",
root / "label" / "DRSeg_train.json",
root / "label" / "DRSeg_val.json",
root / "label" / "DRSeg_test.json",
]
missing = [str(p) for p in required if not p.exists()]
if missing:
raise SystemExit("Missing required DRSeg files:\n" + "\n".join(missing))
print(f"DRSeg is ready: {root}")
if __name__ == "__main__":
main()
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