Robotics
PyTorch
Cosmos
xperience10m_task_baseline_suite
embodied-ai
multimodal
xperience-10m
baseline
evaluation
qwen3-omni
Instructions to use cy0307/ropedia-xperience-10m-task-baselines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use cy0307/ropedia-xperience-10m-task-baselines with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| #!/usr/bin/env python3 | |
| """Build the three foundation-pipeline slide diagrams. | |
| The public foundation-direction visuals intentionally use the direction-slide | |
| sources provided by the project owner, not generated concept art. Clean slide | |
| PNGs are used directly when available; older photo sources are restored only as | |
| fallbacks. The output asset names stay stable for the website, README, and HF | |
| mirrors. | |
| """ | |
| from __future__ import annotations | |
| from dataclasses import dataclass | |
| from pathlib import Path | |
| from PIL import Image, ImageEnhance, ImageFilter, ImageOps | |
| ROOT = Path(__file__).resolve().parents[1] | |
| OUT_DIR = ROOT / "docs/assets/foundation-pipelines" | |
| SOURCE_DIR = OUT_DIR / "source-photos" | |
| SOURCE_SLIDE_DIR = OUT_DIR / "source-slides" | |
| TARGET_WIDTH = 2560 | |
| class PhotoAsset: | |
| source: str | |
| slide_source: str | None | |
| output: str | |
| title: str | |
| brightness: float | |
| contrast: float | |
| color: float | |
| sharpness: float | |
| PHOTOS = [ | |
| PhotoAsset( | |
| source="spatial-intelligence-source.jpg", | |
| slide_source="spatial-intelligence-slide.png", | |
| output="spatial-intelligence-pipeline.png", | |
| title="Spatial intelligence slide diagram", | |
| brightness=1.04, | |
| contrast=1.18, | |
| color=1.08, | |
| sharpness=1.36, | |
| ), | |
| PhotoAsset( | |
| source="human-video-world-model-source.jpg", | |
| slide_source="human-video-world-model-slide.png", | |
| output="human-video-world-model-pipeline.png", | |
| title="Human-video world-model slide diagram", | |
| brightness=1.05, | |
| contrast=1.20, | |
| color=1.08, | |
| sharpness=1.34, | |
| ), | |
| PhotoAsset( | |
| source="vision-language-action-source.jpg", | |
| slide_source="vision-language-action-slide.png", | |
| output="vision-language-action-pipeline.png", | |
| title="Vision-language-action slide diagram", | |
| brightness=1.06, | |
| contrast=1.18, | |
| color=1.09, | |
| sharpness=1.34, | |
| ), | |
| ] | |
| def enhance(asset: PhotoAsset) -> Image.Image: | |
| if asset.slide_source: | |
| slide_path = SOURCE_SLIDE_DIR / asset.slide_source | |
| if slide_path.is_file(): | |
| img = Image.open(slide_path).convert("RGB") | |
| img = ImageOps.exif_transpose(img) | |
| if img.width != TARGET_WIDTH: | |
| scale = TARGET_WIDTH / img.width | |
| target_size = (TARGET_WIDTH, round(img.height * scale)) | |
| img = img.resize(target_size, Image.Resampling.LANCZOS) | |
| return img | |
| source_path = SOURCE_DIR / asset.source | |
| if not source_path.is_file(): | |
| raise FileNotFoundError(f"Missing source slide/photo for {asset.output}: {source_path}") | |
| img = Image.open(source_path).convert("RGB") | |
| img = ImageOps.exif_transpose(img) | |
| img = ImageOps.autocontrast(img, cutoff=0.35) | |
| img = ImageEnhance.Brightness(img).enhance(asset.brightness) | |
| img = ImageEnhance.Contrast(img).enhance(asset.contrast) | |
| img = ImageEnhance.Color(img).enhance(asset.color) | |
| if img.width != TARGET_WIDTH: | |
| scale = TARGET_WIDTH / img.width | |
| target_size = (TARGET_WIDTH, round(img.height * scale)) | |
| img = img.resize(target_size, Image.Resampling.LANCZOS) | |
| # Gentle deblur/edge recovery without hallucinating slide text. | |
| smooth = img.filter(ImageFilter.GaussianBlur(radius=0.55)) | |
| img = Image.blend(smooth, img, 0.68) | |
| img = ImageEnhance.Sharpness(img).enhance(asset.sharpness) | |
| img = img.filter(ImageFilter.UnsharpMask(radius=1.15, percent=135, threshold=3)) | |
| return img | |
| def main() -> int: | |
| OUT_DIR.mkdir(parents=True, exist_ok=True) | |
| for asset in PHOTOS: | |
| output = OUT_DIR / asset.output | |
| image = enhance(asset) | |
| image.save(output, optimize=True, compress_level=9) | |
| print(f"{asset.title}: {output} {image.width}x{image.height} {output.stat().st_size} bytes") | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |