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Deepseek R1 Robotic Reasoning with Checkers
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Deepseek R1 Robotic Reasoning with Checkers
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๐ Fandom.com Community Dataset -
nyuuzyou/fandom
A comprehensive collection of 7.04M wiki pages from Fandom.com communities featuring:
- Full article content and metadata from current pages
- Rich structural data including templates, categories, and links
- Multilingual content across 40+ languages
- Complete metadata including titles and section structure
Content is available under CC-BY-SA 3.0 license, allowing reuse with attribution and share-alike requirements.
Key contents:
- 7.04M wiki articles with full text
- Metadata including templates, categories, sections
- Internal and external link information
- Multi-language support including major world languages
The dataset provides a valuable resource for:
- Text generation and classification tasks
- Topic modeling and categorization
- Cross-language information retrieval
- Wiki structure analysis
All content comes from public Fandom.com community wikis as of February 2025 and maintains original CC-BY-SA 3.0 licensing.
A comprehensive collection of 7.04M wiki pages from Fandom.com communities featuring:
- Full article content and metadata from current pages
- Rich structural data including templates, categories, and links
- Multilingual content across 40+ languages
- Complete metadata including titles and section structure
Content is available under CC-BY-SA 3.0 license, allowing reuse with attribution and share-alike requirements.
Key contents:
- 7.04M wiki articles with full text
- Metadata including templates, categories, sections
- Internal and external link information
- Multi-language support including major world languages
The dataset provides a valuable resource for:
- Text generation and classification tasks
- Topic modeling and categorization
- Cross-language information retrieval
- Wiki structure analysis
All content comes from public Fandom.com community wikis as of February 2025 and maintains original CC-BY-SA 3.0 licensing.
Bigger models of this?
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#6 opened about 1 month ago
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Adzeiros
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14 days ago
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14 days ago
recommended sampler settings
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#4 opened 17 days ago
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plowthat1998
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More detailed description?
#2 opened 24 days ago
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VectorFlow โก: Transform Images into Professional Vector Graphics
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Convert your raster images (JPG, PNG, WEBP) into high-quality vector graphics (SVG, AI) with our easy-to-use tool! Perfect for designers, artists, and anyone needing vector conversions.
๐ฏ Key Features:
Dual format support: SVG and AI output
Real-time preview for both formats
Advanced customization options
Clean, user-friendly interface
Batch processing ready
๐ ๏ธ Advanced Controls:
Color/B&W mode selection
Speckle filtering
Color precision adjustment
Layer management
Curve fitting options
๐ซ Why VectorFlow?
No installation needed
Free to use
Professional-grade output
Simple yet powerful
๐ง Technical Details:
Built with Gradio
Powered by VTracer
Optimized SVG generation
AI format support
๐ Try it now: openfree/VectorFlow
#computervision #vectorgraphics #imageprocessing #svg #design #ai
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We just released the [BiRefNet_HR](
ZhengPeng7/BiRefNet_HR) for general use on higher resolution images, which was trained with images in
HF Model: ZhengPeng7/BiRefNet_HR.
HF Demo: ZhengPeng7/BiRefNet_demo, where you need to choose General-HR and set high resolution.
PyTorch weights & ONNX: in Google Drive and the GitHub release.
Here is a comparison between the results of the original one and the new HR one on HR inputs:
And, the performance of this new HR one and the previous one trained in
2048x2048. If your images are mostly larger than 1024x1024, use BiRefNet_HR for better results! Thanks to
@Freepik
for the kind support of H200s for this huge training.HF Model: ZhengPeng7/BiRefNet_HR.
HF Demo: ZhengPeng7/BiRefNet_demo, where you need to choose General-HR and set high resolution.
PyTorch weights & ONNX: in Google Drive and the GitHub release.
Here is a comparison between the results of the original one and the new HR one on HR inputs:
And, the performance of this new HR one and the previous one trained in
1024x1024 on val set:
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