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
| #!/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() | |