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 os | |
| from pathlib import Path | |
| from huggingface_hub import HfApi, upload_folder | |
| def main(): | |
| parser = argparse.ArgumentParser(description="Upload PixDLM release files to Hugging Face.") | |
| parser.add_argument("--namespace", default="WhynotHug") | |
| parser.add_argument("--model-repo", default="PixDLM") | |
| parser.add_argument("--dataset-repo", default="DRSeg") | |
| parser.add_argument("--private", action="store_true") | |
| args = parser.parse_args() | |
| token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN") | |
| if not token: | |
| raise SystemExit("Set HF_TOKEN in the environment before uploading.") | |
| root = Path(__file__).resolve().parents[1] | |
| api = HfApi(token=token) | |
| model_id = f"{args.namespace}/{args.model_repo}" | |
| dataset_id = f"{args.namespace}/{args.dataset_repo}" | |
| api.create_repo(model_id, repo_type="model", private=args.private, exist_ok=True) | |
| api.create_repo(dataset_id, repo_type="dataset", private=args.private, exist_ok=True) | |
| api.update_repo_settings(model_id, private=args.private, repo_type="model") | |
| api.update_repo_settings(dataset_id, private=args.private, repo_type="dataset") | |
| upload_folder( | |
| repo_id=model_id, | |
| repo_type="model", | |
| folder_path=root, | |
| token=token, | |
| ignore_patterns=[ | |
| ".git/*", | |
| ".DS_Store", | |
| ".cache/*", | |
| "*/.cache/*", | |
| "__pycache__/*", | |
| "*/__pycache__/*", | |
| "data/*", | |
| "checkpoints/*", | |
| "outputs/*", | |
| "logs/*", | |
| "release/tmp/*", | |
| ".hf_token", | |
| "hf_token*", | |
| "*.pt", | |
| "*.pth", | |
| "*.bin", | |
| "*.safetensors", | |
| "*.zip", | |
| "*.tar", | |
| "*.tar.gz", | |
| ], | |
| ) | |
| upload_folder( | |
| repo_id=dataset_id, | |
| repo_type="dataset", | |
| folder_path=root / "release" / "huggingface" / "dataset", | |
| token=token, | |
| ) | |
| print(f"Uploaded model repo: https://huggingface.co/{model_id}") | |
| print(f"Uploaded dataset repo: https://huggingface.co/datasets/{dataset_id}") | |
| if __name__ == "__main__": | |
| main() | |