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 | |
| import re | |
| from pathlib import Path | |
| FORBIDDEN = [ | |
| ("/picassox", re.compile(r"/picassox")), | |
| ("intelligent-cpfs", re.compile(r"intelligent-cpfs")), | |
| ("intern_beauty", re.compile(r"intern_beauty")), | |
| ("10.70.2.17", re.compile(r"10\.70\.2\.17")), | |
| ("/Users/whynotmeitu", re.compile(r"/Users/whynotmeitu")), | |
| ("/opt/tiger", re.compile(r"/opt/tiger")), | |
| ("PointVIS", re.compile(r"PointVIS")), | |
| ("hf token", re.compile(r"hf_[A-Za-z0-9]{20,}")), | |
| ("path_to_", re.compile(r"path_to_")), | |
| ("your-repo", re.compile(r"your-repo")), | |
| ("PATH_TO_", re.compile(r"PATH_TO_")), | |
| ("v137", re.compile(r"v137")), | |
| ] | |
| SKIP_FILES = { | |
| Path("scripts/check_release.py"), | |
| Path(".gitignore"), | |
| } | |
| def main(): | |
| parser = argparse.ArgumentParser(description="Check release tree for private paths and secrets.") | |
| parser.add_argument("root", nargs="?", default=".") | |
| args = parser.parse_args() | |
| root = Path(args.root).resolve() | |
| bad = [] | |
| for path in root.rglob("*"): | |
| if not path.is_file(): | |
| continue | |
| if any(part in {".git", "__pycache__", ".cache"} for part in path.parts): | |
| continue | |
| rel_path = path.relative_to(root) | |
| if rel_path in SKIP_FILES: | |
| continue | |
| if path.suffix.lower() in {".png", ".jpg", ".jpeg", ".bin", ".pt", ".pth", ".model"}: | |
| continue | |
| text = path.read_text(encoding="utf-8", errors="ignore") | |
| for i, line in enumerate(text.splitlines(), 1): | |
| for label, pattern in FORBIDDEN: | |
| if pattern.search(line): | |
| bad.append(f"{rel_path}:{i}: [{label}] {line[:180]}") | |
| break | |
| if bad: | |
| print("Release check failed:") | |
| print("\n".join(bad)) | |
| raise SystemExit(1) | |
| print(f"Release check passed: {root}") | |
| if __name__ == "__main__": | |
| main() | |