DeltaFlow
🏢
Bring your images to life
Eric Chung PRO
DawnC
AI & ML interests
Computer Vision, LLM, Hybrid Architectures, MultiModel, Reinforcement Learning
Recent Activity
updated
a Space
about 5 hours ago
DawnC/DeltaFlow
published
a Space
about 19 hours ago
DawnC/DeltaFlow
replied to
their
post
5 days ago
PawMatchAI — Smarter, Safer, and More Thoughtful Recommendations 🐕✨
🐾 Recommendation system update — deeper reasoning, safer decisions
Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness.
Key technical improvements:
- SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family)
- Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed
-Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%)
-Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice
The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term.
What's improved?
- 🎯 Clearer separation of must-have safety constraints versus flexible preferences
- 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives
- 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting
What's next?
- 🐕 Expanding behavioral and temperament analysis dimensions
- 🐾 Extension to additional species with transfer learning
- 📱 Mobile-optimized deployment for easier access
- 🧩 Enhanced explainability showing why specific breeds are recommended
👉 Try PawMatchAI: https://huggingface.co/spaces/DawnC/PawMatchAI
#AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
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updated
a
Space
about 5 hours ago
published
a
Space
about 19 hours ago
replied to
their
post
5 days ago
Sorry to hear that. Could you let me know which Indian breed your dog is?
The model is currently trained on 124 specific breeds, so if your dog’s breed isn’t in that list, it won’t be recognized. I’m working on expanding the coverage to include more regional breeds based on user feedback like yours.
Thanks for testing and letting me know.
posted
an
update
7 days ago
Post
3774
PawMatchAI — Smarter, Safer, and More Thoughtful Recommendations 🐕✨
🐾 Recommendation system update — deeper reasoning, safer decisions
Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness.
Key technical improvements:
- SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family)
- Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed
-Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%)
-Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice
The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term.
What's improved?
- 🎯 Clearer separation of must-have safety constraints versus flexible preferences
- 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives
- 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting
What's next?
- 🐕 Expanding behavioral and temperament analysis dimensions
- 🐾 Extension to additional species with transfer learning
- 📱 Mobile-optimized deployment for easier access
- 🧩 Enhanced explainability showing why specific breeds are recommended
👉 Try PawMatchAI: DawnC/PawMatchAI
#AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
🐾 Recommendation system update — deeper reasoning, safer decisions
Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness.
Key technical improvements:
- SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family)
- Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed
-Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%)
-Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice
The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term.
What's improved?
- 🎯 Clearer separation of must-have safety constraints versus flexible preferences
- 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives
- 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting
What's next?
- 🐕 Expanding behavioral and temperament analysis dimensions
- 🐾 Extension to additional species with transfer learning
- 📱 Mobile-optimized deployment for easier access
- 🧩 Enhanced explainability showing why specific breeds are recommended
👉 Try PawMatchAI: DawnC/PawMatchAI
#AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
-
- 2 replies
updated
a
Space
7 days ago
updated
a
Space
12 days ago
posted
an
update
13 days ago
Post
5539
Intelligent Inpainting for Precise Creative Control 🎨✨
Transform your images with AI-powered precision! SceneWeaver delivers professional-quality image composition with intelligent background replacement and advanced object manipulation.
What's New in This Update?
🖌️ Object Replacement — Select and transform any element in your scene with natural language prompts while maintaining perfect visual consistency with surrounding content
🗑️ Object Removal — Intelligently remove unwanted objects with context-aware generation that preserves natural lighting, shadows, and scene coherence
🎯 Context-Aware Processing — Advanced inpainting technology ensures seamless integration across all regenerated regions
Core Capabilities
⚡ One-click transformation with smart subject detection, 24 curated professional backgrounds, custom scene generation through text prompts, and studio-quality results powered by BiRefNet, Stable Diffusion XL, and ControlNet Inpainting.
Current Infrastructure & Future Vision
SceneWeaver operates on ZeroGPU with dynamic resource allocation, resulting in extended processing times during peak usage. Based on community demand, I am exploring cloud deployment with dedicated GPU resources for enhanced speed and batch processing capabilities.
Active development focuses on expanding background variety, refining edge quality, and advancing toward intelligent object addition with automatic shadows and reflections—making professional image composition accessible to everyone without technical expertise.
👉 Try it here: DawnC/SceneWeaver
If SceneWeaver helps bring your creative vision to life, please give it a ❤️ — your support influences future development and infrastructure investments!
#AI #Inpainting #DeepLearning #ComputerVision #StableDiffusion #Photography
Transform your images with AI-powered precision! SceneWeaver delivers professional-quality image composition with intelligent background replacement and advanced object manipulation.
What's New in This Update?
🖌️ Object Replacement — Select and transform any element in your scene with natural language prompts while maintaining perfect visual consistency with surrounding content
🗑️ Object Removal — Intelligently remove unwanted objects with context-aware generation that preserves natural lighting, shadows, and scene coherence
🎯 Context-Aware Processing — Advanced inpainting technology ensures seamless integration across all regenerated regions
Core Capabilities
⚡ One-click transformation with smart subject detection, 24 curated professional backgrounds, custom scene generation through text prompts, and studio-quality results powered by BiRefNet, Stable Diffusion XL, and ControlNet Inpainting.
Current Infrastructure & Future Vision
SceneWeaver operates on ZeroGPU with dynamic resource allocation, resulting in extended processing times during peak usage. Based on community demand, I am exploring cloud deployment with dedicated GPU resources for enhanced speed and batch processing capabilities.
Active development focuses on expanding background variety, refining edge quality, and advancing toward intelligent object addition with automatic shadows and reflections—making professional image composition accessible to everyone without technical expertise.
👉 Try it here: DawnC/SceneWeaver
If SceneWeaver helps bring your creative vision to life, please give it a ❤️ — your support influences future development and infrastructure investments!
#AI #Inpainting #DeepLearning #ComputerVision #StableDiffusion #Photography
updated
2
Spaces
13 days ago