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.
Eric Chung PRO
DawnC
AI & ML interests
Computer Vision, LLM, Hybrid Architectures, MultiModel, Reinforcement Learning
Recent Activity
updated
a Space
37 minutes ago
DawnC/DeltaFlow
published
a Space
about 13 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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replied to
their
post
5 days ago
posted
an
update
7 days ago
Post
3773
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
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
posted
an
update
about 1 month ago
Post
3504
SceneWeaver — AI-Powered Background Generation & Image Composition 🎨✨
Transform ordinary portraits into professional studio shots with just one click!
What can SceneWeaver do?
- 📸 Upload any portrait photo and instantly generate stunning, professional-quality backgrounds
- 🎭 Smart Subject Detection — Automatically identifies and extracts people, pets, or objects from your photos, even handling tricky cases like dark clothing and cartoon characters.
- 🌄 Creative Scene Library — Choose from 24 professionally curated backgrounds spanning offices, nature landscapes, urban settings, artistic styles, and seasonal themes, or describe your own custom vision.
- ⚙️ Professional Results — Delivers studio-quality compositions in seconds, saving hours of manual editing work while maintaining natural lighting and color harmony.
What's next?
🎬 Enhanced context-aware generation
🎨 Batch processing for multiple style variations
🔧 Higher resolution output support
🌐 Accessible cloud deployment
Current Status: Under active development with continuous improvements to edge quality, background variety, and processing efficiency.
My goal: To make professional-quality image composition accessible to everyone, whether you're a photographer needing quick background changes, a content creator building your social media presence, or simply someone who wants their photos to look their absolute best.
👉 Try it here: https: DawnC/SceneWeaver
If SceneWeaver helps bring your creative vision to life, please give it a ❤️ to this project — your support inspires ongoing innovation!
#AI #Photography #ImageEditing #ContentCreation #GenerativeAI #DeepLearning
Transform ordinary portraits into professional studio shots with just one click!
What can SceneWeaver do?
- 📸 Upload any portrait photo and instantly generate stunning, professional-quality backgrounds
- 🎭 Smart Subject Detection — Automatically identifies and extracts people, pets, or objects from your photos, even handling tricky cases like dark clothing and cartoon characters.
- 🌄 Creative Scene Library — Choose from 24 professionally curated backgrounds spanning offices, nature landscapes, urban settings, artistic styles, and seasonal themes, or describe your own custom vision.
- ⚙️ Professional Results — Delivers studio-quality compositions in seconds, saving hours of manual editing work while maintaining natural lighting and color harmony.
What's next?
🎬 Enhanced context-aware generation
🎨 Batch processing for multiple style variations
🔧 Higher resolution output support
🌐 Accessible cloud deployment
Current Status: Under active development with continuous improvements to edge quality, background variety, and processing efficiency.
My goal: To make professional-quality image composition accessible to everyone, whether you're a photographer needing quick background changes, a content creator building your social media presence, or simply someone who wants their photos to look their absolute best.
👉 Try it here: https: DawnC/SceneWeaver
If SceneWeaver helps bring your creative vision to life, please give it a ❤️ to this project — your support inspires ongoing innovation!
#AI #Photography #ImageEditing #ContentCreation #GenerativeAI #DeepLearning
replied to
their
post
about 2 months ago
Glad u like it !
posted
an
update
about 2 months ago
Post
2931
Pixcribe — AI-Powered Social Media Caption Generator 📸✨
Transform your images into compelling stories with intelligent multi-model analysis!
What can Pixcribe do?
📸 Upload photos (up to 10) to get instant AI-generated captions in Traditional Chinese and English
- 🏷️ Brand Recognition — Detects logos and brand elements through visual detection, semantic analysis, and OCR verification.
- 🎨 Scene Understanding — Analyzes composition, lighting conditions, and visual aesthetics to capture your image's mood and context.
- 🔍 Smart Text Extraction — Identifies and incorporates text from your images into captions seamlessly.
- ⚡ Multi-Model Intelligence — Combines YOLOv11 object detection, OpenCLIP semantic understanding, EasyOCR text recognition, U2-Net saliency detection, and Qwen2.5-VL-7B caption generation.
What's next?
🎬 Video processing capabilities
🌐 Enhanced multilingual support
🎯 Interactive caption refinement with user feedback
⚡ Real-time processing optimizations
- Current Status: Under active development — continuously improving brand recognition accuracy and expanding analytical capabilities.
- My goal: To empower content creators, marketers, and social media managers by automating caption generation while maintaining creative quality and cultural authenticity.
👉 Try it here: DawnC/Pixcribe
If you find Pixcribe helpful, please give it a ❤️ , your support drives continuous innovation!
#ComputerVision #VisionLanguageModel #DeepLearning #MachineLearning #ContentCreation #AI #SocialMedia
Transform your images into compelling stories with intelligent multi-model analysis!
What can Pixcribe do?
📸 Upload photos (up to 10) to get instant AI-generated captions in Traditional Chinese and English
- 🏷️ Brand Recognition — Detects logos and brand elements through visual detection, semantic analysis, and OCR verification.
- 🎨 Scene Understanding — Analyzes composition, lighting conditions, and visual aesthetics to capture your image's mood and context.
- 🔍 Smart Text Extraction — Identifies and incorporates text from your images into captions seamlessly.
- ⚡ Multi-Model Intelligence — Combines YOLOv11 object detection, OpenCLIP semantic understanding, EasyOCR text recognition, U2-Net saliency detection, and Qwen2.5-VL-7B caption generation.
What's next?
🎬 Video processing capabilities
🌐 Enhanced multilingual support
🎯 Interactive caption refinement with user feedback
⚡ Real-time processing optimizations
- Current Status: Under active development — continuously improving brand recognition accuracy and expanding analytical capabilities.
- My goal: To empower content creators, marketers, and social media managers by automating caption generation while maintaining creative quality and cultural authenticity.
👉 Try it here: DawnC/Pixcribe
If you find Pixcribe helpful, please give it a ❤️ , your support drives continuous innovation!
#ComputerVision #VisionLanguageModel #DeepLearning #MachineLearning #ContentCreation #AI #SocialMedia
-
- 2 replies
posted
an
update
4 months ago
Post
6702
PawMatchAI — Now with SBERT-Powered Recommendations! 🐶✨
⭐️ NEW: Description-based recommendations are here!
Just type in your lifestyle or preferences (e.g. “I live in an apartment and want a quiet dog”), and PawMatchAI uses SBERT semantic embeddings to understand your needs and suggest compatible breeds.
What can PawMatchAI do today?
📸 Upload a photo to identify your dog from 124 breeds with detailed info.
⚖️ Compare two breeds side-by-side, from grooming needs to health insights.
📊 Visualize breed traits with radar and comparison charts.
🎨 Try Style Transfer to turn your dog’s photo into anime, watercolor, cyberpunk, and more.
What’s next?
🎯 More fine-tuned recommendations.
📱 Mobile-friendly deployment.
🐾 Expansion to additional species.
My goal:
To make breed discovery not only accurate but also interactive and fun — combining computer vision, semantic understanding, and creativity to help people find their perfect companion.
👉 Try it here:
DawnC/PawMatchAI
If you enjoy PawMatchAI, please give the project a ❤️ — it really helps and keeps me motivated to keep improving!
#ComputerVision #SBERT #DeepLearning #MachineLearning #TechForLife
⭐️ NEW: Description-based recommendations are here!
Just type in your lifestyle or preferences (e.g. “I live in an apartment and want a quiet dog”), and PawMatchAI uses SBERT semantic embeddings to understand your needs and suggest compatible breeds.
What can PawMatchAI do today?
📸 Upload a photo to identify your dog from 124 breeds with detailed info.
⚖️ Compare two breeds side-by-side, from grooming needs to health insights.
📊 Visualize breed traits with radar and comparison charts.
🎨 Try Style Transfer to turn your dog’s photo into anime, watercolor, cyberpunk, and more.
What’s next?
🎯 More fine-tuned recommendations.
📱 Mobile-friendly deployment.
🐾 Expansion to additional species.
My goal:
To make breed discovery not only accurate but also interactive and fun — combining computer vision, semantic understanding, and creativity to help people find their perfect companion.
👉 Try it here:
DawnC/PawMatchAI
If you enjoy PawMatchAI, please give the project a ❤️ — it really helps and keeps me motivated to keep improving!
#ComputerVision #SBERT #DeepLearning #MachineLearning #TechForLife
replied to
their
post
6 months ago
Thanks! So glad you enjoyed the technical deep dive.
replied to
their
post
6 months ago
Thank you for the kind words! That's a great suggestion, I'll definitely look into it !
posted
an
update
6 months ago
Post
4527
🎯 Excited to share my comprehensive deep dive into VisionScout's multimodal AI architecture, now published as a three-part series on Towards Data Science!
This isn't just another computer vision project. VisionScout represents a fundamental shift from simple object detection to genuine scene understanding, where four specialized AI models work together to interpret what's actually happening in an image.
🏗️ Part 1: Architecture Foundation
How careful system design transforms independent models into collaborative intelligence through proper layering and coordination strategies.
⚙️ Part 2: Deep Technical Implementation
The five core algorithms powering the system: dynamic weight adjustment, attention mechanisms, statistical methods, lighting analysis, and CLIP's zero-shot learning.
🌍 Part 3: Real-World Validation
Concrete case studies from indoor spaces to cultural landmarks, demonstrating how integrated systems deliver insights no single model could achieve.
What makes this valuable:
The series shows how intelligent orchestration creates emergent capabilities. When YOLOv8, CLIP, Places365, and Llama 3.2 collaborate, the result is genuine scene comprehension beyond simple detection.
⭐️ Try it yourself:
DawnC/VisionScout
Read the complete series:
📖 Part 1: https://towardsdatascience.com/the-art-of-multimodal-ai-system-design/
📖 Part 2: https://towardsdatascience.com/four-ai-minds-in-concert-a-deep-dive-into-multimodal-ai-fusion/
📖 Part 3: https://towardsdatascience.com/scene-understanding-in-action-real-world-validation-of-multimodal-ai-integration/
#AI #DeepLearning #MultimodalAI #ComputerVision #SceneUnderstanding #TechForLife
This isn't just another computer vision project. VisionScout represents a fundamental shift from simple object detection to genuine scene understanding, where four specialized AI models work together to interpret what's actually happening in an image.
🏗️ Part 1: Architecture Foundation
How careful system design transforms independent models into collaborative intelligence through proper layering and coordination strategies.
⚙️ Part 2: Deep Technical Implementation
The five core algorithms powering the system: dynamic weight adjustment, attention mechanisms, statistical methods, lighting analysis, and CLIP's zero-shot learning.
🌍 Part 3: Real-World Validation
Concrete case studies from indoor spaces to cultural landmarks, demonstrating how integrated systems deliver insights no single model could achieve.
What makes this valuable:
The series shows how intelligent orchestration creates emergent capabilities. When YOLOv8, CLIP, Places365, and Llama 3.2 collaborate, the result is genuine scene comprehension beyond simple detection.
⭐️ Try it yourself:
DawnC/VisionScout
Read the complete series:
📖 Part 1: https://towardsdatascience.com/the-art-of-multimodal-ai-system-design/
📖 Part 2: https://towardsdatascience.com/four-ai-minds-in-concert-a-deep-dive-into-multimodal-ai-fusion/
📖 Part 3: https://towardsdatascience.com/scene-understanding-in-action-real-world-validation-of-multimodal-ai-integration/
#AI #DeepLearning #MultimodalAI #ComputerVision #SceneUnderstanding #TechForLife
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- 6 replies
posted
an
update
7 months ago
Post
3749
🚀 I'm excited to share a recent update to VisionScout, a system built to help machines do more than just detect — but actually understand what’s happening in a scene.
🎯 At its core, VisionScout is about deep scene interpretation.
It combines the sharp detection of YOLOv8, the semantic awareness of CLIP, the environmental grounding of Places365, and the expressive fluency of Llama 3.2.
Together, they deliver more than bounding boxes, they produce rich narratives about layout, lighting, activities, and contextual cues.
🏞️ For example:
- CLIP’s zero-shot capability recognizes cultural landmarks without any task-specific training
- Places365 helps anchor the scene into one of 365 categories, refining lighting interpretation and spatial understanding. It also assists in distinguishing indoor vs. outdoor scenes and enables lighting condition classification such as “sunset”, “sunrise”, or “indoor commercial”
- Llama 3.2 turns structured analysis into human-readable, context-rich descriptions
🎬 So where does video fit in?
While the current video module focuses on structured, statistical analysis, it builds on the same architectural principles as the image pipeline.
This update enables:
- Frame-by-frame object tracking and timeline breakdown
- Confidence-based quality grading
- Aggregated object counts and time-based appearance patterns
These features offer a preview of what’s coming, extending scene reasoning into the temporal domain.
🔧 Curious how it all works?
Try the system here:
DawnC/VisionScout
Explore the source code and technical implementation:
https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/VisionScout
🛰️ VisionScout isn’t just about what the machine sees.
It’s about helping it explain — fluently, factually, and meaningfully.
#SceneUnderstanding #ComputerVision #DeepLearning #YOLO #CLIP #Llama3 #Places365 #MultiModal #TechForLife
🎯 At its core, VisionScout is about deep scene interpretation.
It combines the sharp detection of YOLOv8, the semantic awareness of CLIP, the environmental grounding of Places365, and the expressive fluency of Llama 3.2.
Together, they deliver more than bounding boxes, they produce rich narratives about layout, lighting, activities, and contextual cues.
🏞️ For example:
- CLIP’s zero-shot capability recognizes cultural landmarks without any task-specific training
- Places365 helps anchor the scene into one of 365 categories, refining lighting interpretation and spatial understanding. It also assists in distinguishing indoor vs. outdoor scenes and enables lighting condition classification such as “sunset”, “sunrise”, or “indoor commercial”
- Llama 3.2 turns structured analysis into human-readable, context-rich descriptions
🎬 So where does video fit in?
While the current video module focuses on structured, statistical analysis, it builds on the same architectural principles as the image pipeline.
This update enables:
- Frame-by-frame object tracking and timeline breakdown
- Confidence-based quality grading
- Aggregated object counts and time-based appearance patterns
These features offer a preview of what’s coming, extending scene reasoning into the temporal domain.
🔧 Curious how it all works?
Try the system here:
DawnC/VisionScout
Explore the source code and technical implementation:
https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/VisionScout
🛰️ VisionScout isn’t just about what the machine sees.
It’s about helping it explain — fluently, factually, and meaningfully.
#SceneUnderstanding #ComputerVision #DeepLearning #YOLO #CLIP #Llama3 #Places365 #MultiModal #TechForLife
posted
an
update
7 months ago
Post
3267
VisionScout Major Update: Enhanced Precision Through Multi-Modal AI Integration
I'm excited to share significant improvements to VisionScout that substantially enhance accuracy and analytical capabilities.
⭐️ Key Enhancements
- CLIP Zero-Shot Landmark Detection: The system now identifies famous landmarks and architectural features without requiring specific training data, expanding scene understanding beyond generic object detection.
- Places365 Environmental Classification: Integration of MIT's Places365 model provides robust scene baseline classification across 365 categories, significantly improving lighting analysis accuracy and overall scene identification precision.
- Enhanced Multi-Modal Fusion: Advanced algorithms now dynamically combine insights from YOLOv8, CLIP, and Places365 to optimize accuracy across diverse scenarios.
- Refined LLM Narratives: Llama 3.2 integration continues to transform analytical data into fluent, contextually rich descriptions while maintaining strict factual accuracy.
🎯 Future Development Focus
Accuracy remains the primary development priority, with ongoing enhancements to multi-modal fusion capabilities. Future work will advance video analysis beyond current object tracking foundations to include comprehensive temporal scene understanding and dynamic narrative generation.
Try it out 👉 DawnC/VisionScout
If you find this update valuable, a Like❤️ or comment means a lot!
#LLM #ComputerVision #MachineLearning #MultiModel #TechForLife
I'm excited to share significant improvements to VisionScout that substantially enhance accuracy and analytical capabilities.
⭐️ Key Enhancements
- CLIP Zero-Shot Landmark Detection: The system now identifies famous landmarks and architectural features without requiring specific training data, expanding scene understanding beyond generic object detection.
- Places365 Environmental Classification: Integration of MIT's Places365 model provides robust scene baseline classification across 365 categories, significantly improving lighting analysis accuracy and overall scene identification precision.
- Enhanced Multi-Modal Fusion: Advanced algorithms now dynamically combine insights from YOLOv8, CLIP, and Places365 to optimize accuracy across diverse scenarios.
- Refined LLM Narratives: Llama 3.2 integration continues to transform analytical data into fluent, contextually rich descriptions while maintaining strict factual accuracy.
🎯 Future Development Focus
Accuracy remains the primary development priority, with ongoing enhancements to multi-modal fusion capabilities. Future work will advance video analysis beyond current object tracking foundations to include comprehensive temporal scene understanding and dynamic narrative generation.
Try it out 👉 DawnC/VisionScout
If you find this update valuable, a Like❤️ or comment means a lot!
#LLM #ComputerVision #MachineLearning #MultiModel #TechForLife
replied to
their
post
8 months ago
Glad to hear !
posted
an
update
8 months ago
Post
2578
🚀 VisionScout Now Speaks More Like Me — Thanks to LLMs!
I'm thrilled to share a major update to VisionScout, my end-to-end vision system.
Beyond robust object detection (YOLOv8) and semantic context (CLIP), VisionScout now features a powerful LLM-based scene narrator (Llama 3.2), improving the clarity, accuracy, and fluidity of scene understanding.
This isn’t about replacing the pipeline , it’s about giving it a better voice. ✨
⭐️ What the LLM Brings
Fluent, Natural Descriptions:
The LLM transforms structured outputs into human-readable narratives.
Smarter Contextual Flow:
It weaves lighting, objects, zones, and insights into a unified story.
Grounded Expression:
Carefully prompt-engineered to stay factual — it enhances, not hallucinates.
Helpful Discrepancy Handling:
When YOLO and CLIP diverge, the LLM adds clarity through reasoning.
VisionScout Still Includes:
🖼️ YOLOv8-based detection (Nano / Medium / XLarge)
📊 Real-time stats & confidence insights
🧠 Scene understanding via multimodal fusion
🎬 Video analysis & object tracking
🎯 My Goal
I built VisionScout to bridge the gap between raw vision data and meaningful understanding.
This latest LLM integration helps the system communicate its insights in a way that’s more accurate, more human, and more useful.
Try it out 👉 DawnC/VisionScout
If you find this update valuable, a Like❤️ or comment means a lot!
#LLM #ComputerVision #MachineLearning #TechForLife
I'm thrilled to share a major update to VisionScout, my end-to-end vision system.
Beyond robust object detection (YOLOv8) and semantic context (CLIP), VisionScout now features a powerful LLM-based scene narrator (Llama 3.2), improving the clarity, accuracy, and fluidity of scene understanding.
This isn’t about replacing the pipeline , it’s about giving it a better voice. ✨
⭐️ What the LLM Brings
Fluent, Natural Descriptions:
The LLM transforms structured outputs into human-readable narratives.
Smarter Contextual Flow:
It weaves lighting, objects, zones, and insights into a unified story.
Grounded Expression:
Carefully prompt-engineered to stay factual — it enhances, not hallucinates.
Helpful Discrepancy Handling:
When YOLO and CLIP diverge, the LLM adds clarity through reasoning.
VisionScout Still Includes:
🖼️ YOLOv8-based detection (Nano / Medium / XLarge)
📊 Real-time stats & confidence insights
🧠 Scene understanding via multimodal fusion
🎬 Video analysis & object tracking
🎯 My Goal
I built VisionScout to bridge the gap between raw vision data and meaningful understanding.
This latest LLM integration helps the system communicate its insights in a way that’s more accurate, more human, and more useful.
Try it out 👉 DawnC/VisionScout
If you find this update valuable, a Like❤️ or comment means a lot!
#LLM #ComputerVision #MachineLearning #TechForLife
-
-
- 2 replies
posted
an
update
8 months ago
Post
3484
PawMatchAI 🐾: The Complete Dog Breed Platform
PawMatchAI offers a comprehensive suite of features designed for dog enthusiasts and prospective owners alike. This all-in-one platform delivers five essential tools to enhance your canine experience:
1. 🔍Breed Detection: Upload any dog photo and the AI accurately identifies breeds from an extensive database of 124+ different dog breeds. The system detects dogs in the image and provides confident breed identification results.
2.📊Breed Information: Access detailed profiles for each breed covering exercise requirements, typical lifespan, grooming needs, health considerations, and noise behavior - giving you complete understanding of any breed's characteristics.
3.📋 Breed Comparison : Compare any two breeds side-by-side with intuitive visualizations highlighting differences in care requirements, personality traits, health factors, and more - perfect for making informed decisions.
4.💡 Breed Recommendation: Receive personalized breed suggestions based on your lifestyle preferences. The sophisticated matching system evaluates compatibility across multiple factors including living space, exercise capacity, experience level, and family situation.
5.🎨 Style Transfer: Transform your dog photos into artistic masterpieces with five distinct styles: Japanese Anime, Classic Cartoon, Oil Painting, Watercolor, and Cyberpunk - adding a creative dimension to your pet photography.
👋Explore PawMatchAI today:
DawnC/PawMatchAI
If you enjoy this project or find it valuable for your canine companions, I'd greatly appreciate your support with a Like❤️ for this project.
#ArtificialIntelligence #MachineLearning #ComputerVision #PetTech #TechForLife
PawMatchAI offers a comprehensive suite of features designed for dog enthusiasts and prospective owners alike. This all-in-one platform delivers five essential tools to enhance your canine experience:
1. 🔍Breed Detection: Upload any dog photo and the AI accurately identifies breeds from an extensive database of 124+ different dog breeds. The system detects dogs in the image and provides confident breed identification results.
2.📊Breed Information: Access detailed profiles for each breed covering exercise requirements, typical lifespan, grooming needs, health considerations, and noise behavior - giving you complete understanding of any breed's characteristics.
3.📋 Breed Comparison : Compare any two breeds side-by-side with intuitive visualizations highlighting differences in care requirements, personality traits, health factors, and more - perfect for making informed decisions.
4.💡 Breed Recommendation: Receive personalized breed suggestions based on your lifestyle preferences. The sophisticated matching system evaluates compatibility across multiple factors including living space, exercise capacity, experience level, and family situation.
5.🎨 Style Transfer: Transform your dog photos into artistic masterpieces with five distinct styles: Japanese Anime, Classic Cartoon, Oil Painting, Watercolor, and Cyberpunk - adding a creative dimension to your pet photography.
👋Explore PawMatchAI today:
DawnC/PawMatchAI
If you enjoy this project or find it valuable for your canine companions, I'd greatly appreciate your support with a Like❤️ for this project.
#ArtificialIntelligence #MachineLearning #ComputerVision #PetTech #TechForLife
reacted to
wolfram's
post with 🔥
8 months ago
Post
7773
Finally finished my extensive **Qwen 3 evaluations** across a range of formats and quantisations, focusing on **MMLU-Pro** (Computer Science).
A few take-aways stood out - especially for those interested in local deployment and performance trade-offs:
1️⃣ **Qwen3-235B-A22B** (via Fireworks API) tops the table at **83.66%** with ~55 tok/s.
2️⃣ But the **30B-A3B Unsloth** quant delivered **82.20%** while running locally at ~45 tok/s and with zero API spend.
3️⃣ The same Unsloth build is ~5x faster than Qwen's **Qwen3-32B**, which scores **82.20%** as well yet crawls at <10 tok/s.
4️⃣ On Apple silicon, the **30B MLX** port hits **79.51%** while sustaining ~64 tok/s - arguably today's best speed/quality trade-off for Mac setups.
5️⃣ The **0.6B** micro-model races above 180 tok/s but tops out at **37.56%** - that's why it's not even on the graph (50 % performance cut-off).
All local runs were done with LM Studio on an M4 MacBook Pro, using Qwen's official recommended settings.
**Conclusion:** Quantised 30B models now get you ~98 % of frontier-class accuracy - at a fraction of the latency, cost, and energy. For most local RAG or agent workloads, they're not just good enough - they're the new default.
Well done, Qwen - you really whipped the llama's ass! And to OpenAI: for your upcoming open model, please make it MoE, with toggleable reasoning, and release it in many sizes. *This* is the future!
A few take-aways stood out - especially for those interested in local deployment and performance trade-offs:
1️⃣ **Qwen3-235B-A22B** (via Fireworks API) tops the table at **83.66%** with ~55 tok/s.
2️⃣ But the **30B-A3B Unsloth** quant delivered **82.20%** while running locally at ~45 tok/s and with zero API spend.
3️⃣ The same Unsloth build is ~5x faster than Qwen's **Qwen3-32B**, which scores **82.20%** as well yet crawls at <10 tok/s.
4️⃣ On Apple silicon, the **30B MLX** port hits **79.51%** while sustaining ~64 tok/s - arguably today's best speed/quality trade-off for Mac setups.
5️⃣ The **0.6B** micro-model races above 180 tok/s but tops out at **37.56%** - that's why it's not even on the graph (50 % performance cut-off).
All local runs were done with LM Studio on an M4 MacBook Pro, using Qwen's official recommended settings.
**Conclusion:** Quantised 30B models now get you ~98 % of frontier-class accuracy - at a fraction of the latency, cost, and energy. For most local RAG or agent workloads, they're not just good enough - they're the new default.
Well done, Qwen - you really whipped the llama's ass! And to OpenAI: for your upcoming open model, please make it MoE, with toggleable reasoning, and release it in many sizes. *This* is the future!
- 4 replies
posted
an
update
8 months ago
Post
5368
VisionScout — Now with Video Analysis! 🚀
I’m excited to announce a major update to VisionScout, my interactive vision tool that now supports VIDEO PROCESSING, in addition to powerful object detection and scene understanding!
⭐️ NEW: Video Analysis Is Here!
🎬 Upload any video file to detect and track objects using YOLOv8.
⏱️ Customize processing intervals to balance speed and thoroughness.
📊 Get comprehensive statistics and summaries showing object appearances across the entire video.
What else can VisionScout do?
🖼️ Analyze any image and detect 80 object types with YOLOv8.
🔄 Switch between Nano, Medium, and XLarge models for speed or accuracy.
🎯 Filter by object classes (people, vehicles, animals, etc.) to focus on what matters.
📊 View detailed stats on detections, confidence levels, and distributions.
🧠 Understand scenes — interpreting environments and potential activities.
⚠️ Automatically identify possible safety concerns based on detected objects.
What’s coming next?
🔎 Expanding YOLO’s object categories.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To bridge the gap between raw detection and meaningful interpretation.
I’m constantly exploring ways to help machines not just "see" but truly understand context — and to make these advanced tools accessible to everyone, regardless of technical background.
Try it now! 🖼️👉 DawnC/VisionScout
If you enjoy VisionScout, a ❤️ Like for this project or feedback would mean a lot and keeps me motivated to keep building and improving!
#ComputerVision #ObjectDetection #VideoAnalysis #YOLO #SceneUnderstanding #MachineLearning #TechForLife
I’m excited to announce a major update to VisionScout, my interactive vision tool that now supports VIDEO PROCESSING, in addition to powerful object detection and scene understanding!
⭐️ NEW: Video Analysis Is Here!
🎬 Upload any video file to detect and track objects using YOLOv8.
⏱️ Customize processing intervals to balance speed and thoroughness.
📊 Get comprehensive statistics and summaries showing object appearances across the entire video.
What else can VisionScout do?
🖼️ Analyze any image and detect 80 object types with YOLOv8.
🔄 Switch between Nano, Medium, and XLarge models for speed or accuracy.
🎯 Filter by object classes (people, vehicles, animals, etc.) to focus on what matters.
📊 View detailed stats on detections, confidence levels, and distributions.
🧠 Understand scenes — interpreting environments and potential activities.
⚠️ Automatically identify possible safety concerns based on detected objects.
What’s coming next?
🔎 Expanding YOLO’s object categories.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To bridge the gap between raw detection and meaningful interpretation.
I’m constantly exploring ways to help machines not just "see" but truly understand context — and to make these advanced tools accessible to everyone, regardless of technical background.
Try it now! 🖼️👉 DawnC/VisionScout
If you enjoy VisionScout, a ❤️ Like for this project or feedback would mean a lot and keeps me motivated to keep building and improving!
#ComputerVision #ObjectDetection #VideoAnalysis #YOLO #SceneUnderstanding #MachineLearning #TechForLife
-
- 2 replies
posted
an
update
8 months ago
Post
2962
VisionScout — Now with Scene Understanding! 🚀
I'm excited to share a major update to VisionScout, my interactive vision tool that combines powerful object detection with emerging scene understanding capabilities! 👀🔍
What can VisionScout do today?
🖼️ Upload any image and detect 80 object types using YOLOv8.
🔄 Instantly switch between Nano, Medium, and XLarge models depending on speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
📊 View detailed statistics on detected objects, confidence levels, and spatial distribution.
⭐️ NEW: Scene understanding layer now added!
- Automatically interprets the scene based on detected objects.
- Uses a combination of rule-based reasoning and CLIP-powered semantic validation.
- Outputs descriptions, possible activities, and even safety concerns.
What’s coming next?
🔎 Expanding YOLO’s object categories.
🎥 Adding video processing and multi-frame object tracking.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To make advanced vision tools accessible to everyone, from beginners to experts , while continuing to push for more accurate and meaningful scene interpretation.
Try it yourself! 🖼️
👉 DawnC/VisionScout
If you enjoy VisionScout, feel free to give the project a ❤️, it really helps and keeps me motivated to keep building and improving!
Stay tuned for more updates!
#ComputerVision #ObjectDetection #YOLO #SceneUnderstanding #MachineLearning #TechForLife
I'm excited to share a major update to VisionScout, my interactive vision tool that combines powerful object detection with emerging scene understanding capabilities! 👀🔍
What can VisionScout do today?
🖼️ Upload any image and detect 80 object types using YOLOv8.
🔄 Instantly switch between Nano, Medium, and XLarge models depending on speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
📊 View detailed statistics on detected objects, confidence levels, and spatial distribution.
⭐️ NEW: Scene understanding layer now added!
- Automatically interprets the scene based on detected objects.
- Uses a combination of rule-based reasoning and CLIP-powered semantic validation.
- Outputs descriptions, possible activities, and even safety concerns.
What’s coming next?
🔎 Expanding YOLO’s object categories.
🎥 Adding video processing and multi-frame object tracking.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To make advanced vision tools accessible to everyone, from beginners to experts , while continuing to push for more accurate and meaningful scene interpretation.
Try it yourself! 🖼️
👉 DawnC/VisionScout
If you enjoy VisionScout, feel free to give the project a ❤️, it really helps and keeps me motivated to keep building and improving!
Stay tuned for more updates!
#ComputerVision #ObjectDetection #YOLO #SceneUnderstanding #MachineLearning #TechForLife
posted
an
update
8 months ago
Post
4273
I'm excited to introduce VisionScout —an interactive vision tool that makes computer vision both accessible and powerful! 👀🔍
What can VisionScout do right now?
🖼️ Upload any image and detect 80 different object types using YOLOv8.
🔄 Instantly switch between Nano, Medium, and XLarge models depending on your speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
📊 View detailed statistics about detected objects, confidence levels, and spatial distribution.
🎨 Enjoy a clean, intuitive interface with responsive design and enhanced visualizations.
What's next?
I'm working on exciting updates:
- Support for more models
- Video processing and object tracking across frames
- Faster real-time detection
- Improved mobile responsiveness
The goal is to build a complete but user-friendly vision toolkit for both beginners and advanced users.
Try it yourself! 🚀
DawnC/VisionScout
I'd love to hear your feedback , what features would you find most useful? Any specific use cases you'd love to see supported?
Give it a try and let me know your thoughts in the comments! Stay tuned for future updates.
#ComputerVision #ObjectDetection #YOLO #MachineLearning #TechForLife
What can VisionScout do right now?
🖼️ Upload any image and detect 80 different object types using YOLOv8.
🔄 Instantly switch between Nano, Medium, and XLarge models depending on your speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
📊 View detailed statistics about detected objects, confidence levels, and spatial distribution.
🎨 Enjoy a clean, intuitive interface with responsive design and enhanced visualizations.
What's next?
I'm working on exciting updates:
- Support for more models
- Video processing and object tracking across frames
- Faster real-time detection
- Improved mobile responsiveness
The goal is to build a complete but user-friendly vision toolkit for both beginners and advanced users.
Try it yourself! 🚀
DawnC/VisionScout
I'd love to hear your feedback , what features would you find most useful? Any specific use cases you'd love to see supported?
Give it a try and let me know your thoughts in the comments! Stay tuned for future updates.
#ComputerVision #ObjectDetection #YOLO #MachineLearning #TechForLife
reacted to
John6666's
post with 👍
9 months ago
Post
28563
I used up my Zero GPU Quota yesterday (about 12 hours ago). At the time, I got a message saying “Retry at 13:45 (approx.)”, but now it's just changed to “Retry at 03:22”.
Anyway, everyone, let's be careful not to use up our Quota...
Related: https://huggingface.co/posts/Keltezaa/754755723533287#67e6ed5e3394f1ed9ca41dbd
Anyway, everyone, let's be careful not to use up our Quota...
Related: https://huggingface.co/posts/Keltezaa/754755723533287#67e6ed5e3394f1ed9ca41dbd
- 1 reply