Thanks! So glad you enjoyed the technical deep dive.
Eric Chung PRO
DawnC
AI & ML interests
Computer Vision, LLM, Hybrid Architectures, MultiModel, Reinforcement Learning
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liked
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18 days ago
tonyassi/voice-clone
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an
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18 days ago
(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware
replied to
their
post
20 days ago
🎯 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:
https://huggingface.co/spaces/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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replied to
their
post
20 days ago
replied to
their
post
20 days ago
Thank you for the kind words! That's a great suggestion, I'll definitely look into it !
posted
an
update
22 days ago
Post
4443
🎯 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
-
- 6 replies
posted
an
update
about 2 months ago
Post
3713
🚀 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
2 months ago
Post
3266
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
3 months ago
Glad to hear !
posted
an
update
3 months ago
Post
2577
🚀 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
3 months ago
Post
3481
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 🔥
3 months ago
Post
7413
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
3 months ago
Post
5348
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
3 months ago
Post
2958
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
3 months ago
Post
4267
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
seawolf2357's
post with ❤️
4 months ago
Post
6733
🔥 AgenticAI: The Ultimate Multimodal AI with 16 MBTI Girlfriend Personas! 🔥
Hello AI community! Today, our team is thrilled to introduce AgenticAI, an innovative open-source AI assistant that combines deep technical capabilities with uniquely personalized interaction. 💘
🛠️ MBTI 16 Types SPACES Collections link
seawolf2357/heartsync-mbti-67f793d752ef1fa542e16560
✨ 16 MBTI Girlfriend Personas
Complete MBTI Implementation: All 16 MBTI female personas modeled after iconic characters (Dana Scully, Lara Croft, etc.)
Persona Depth: Customize age groups and thinking patterns for hyper-personalized AI interactions
Personality Consistency: Each MBTI type demonstrates consistent problem-solving approaches, conversation patterns, and emotional expressions
🚀 Cutting-Edge Multimodal Capabilities
Integrated File Analysis: Deep analysis and cross-referencing of images, videos, CSV, PDF, and TXT files
Advanced Image Understanding: Interprets complex diagrams, mathematical equations, charts, and tables
Video Processing: Extracts key frames from videos and understands contextual meaning
Document RAG: Intelligent analysis and summarization of PDF/CSV/TXT files
💡 Deep Research & Knowledge Enhancement
Real-time Web Search: SerpHouse API integration for latest information retrieval and citation
Deep Reasoning Chains: Step-by-step inference process for solving complex problems
Academic Analysis: In-depth approach to mathematical problems, scientific questions, and data analysis
Structured Knowledge Generation: Systematic code, data analysis, and report creation
🖼️ Creative Generation Engine
FLUX Image Generation: Custom image creation reflecting the selected MBTI persona traits
Data Visualization: Automatic generation of code for visualizing complex datasets
Creative Writing: Story and scenario writing matching the selected persona's style
Hello AI community! Today, our team is thrilled to introduce AgenticAI, an innovative open-source AI assistant that combines deep technical capabilities with uniquely personalized interaction. 💘
🛠️ MBTI 16 Types SPACES Collections link
seawolf2357/heartsync-mbti-67f793d752ef1fa542e16560
✨ 16 MBTI Girlfriend Personas
Complete MBTI Implementation: All 16 MBTI female personas modeled after iconic characters (Dana Scully, Lara Croft, etc.)
Persona Depth: Customize age groups and thinking patterns for hyper-personalized AI interactions
Personality Consistency: Each MBTI type demonstrates consistent problem-solving approaches, conversation patterns, and emotional expressions
🚀 Cutting-Edge Multimodal Capabilities
Integrated File Analysis: Deep analysis and cross-referencing of images, videos, CSV, PDF, and TXT files
Advanced Image Understanding: Interprets complex diagrams, mathematical equations, charts, and tables
Video Processing: Extracts key frames from videos and understands contextual meaning
Document RAG: Intelligent analysis and summarization of PDF/CSV/TXT files
💡 Deep Research & Knowledge Enhancement
Real-time Web Search: SerpHouse API integration for latest information retrieval and citation
Deep Reasoning Chains: Step-by-step inference process for solving complex problems
Academic Analysis: In-depth approach to mathematical problems, scientific questions, and data analysis
Structured Knowledge Generation: Systematic code, data analysis, and report creation
🖼️ Creative Generation Engine
FLUX Image Generation: Custom image creation reflecting the selected MBTI persona traits
Data Visualization: Automatic generation of code for visualizing complex datasets
Creative Writing: Story and scenario writing matching the selected persona's style
- 1 reply
reacted to
John6666's
post with 👍
4 months ago
Post
15276
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
posted
an
update
4 months ago
Post
2584
New in PawMatchAI🐾 : Turn Your Dog Photos into Art!
I’m excited to introduce a brand-new creative feature — Dog Style Transfer is now live on PawMatchAI!
Just upload your dog’s photo and transform it into 5 artistic styles:
🌸 Japanese Anime
📚 Classic Cartoon
🖼️ Oil Painting
🎨 Watercolor
🌆 Cyberpunk
All powered by Stable Diffusion and enhanced with smart prompt tuning to preserve your dog’s unique traits and breed identity , so the artwork stays true to your furry friend.
Whether you're creating a custom portrait or just having fun, this feature brings your pet photos to life in completely new ways.
And here’s a little secret: although it’s designed with dogs in mind, it actually works on any photo — cats, plush toys, even humans. Feel free to experiment!
Results may not always be perfectly accurate, sometimes your photo might come back looking a little different, or even beyond your imagination. But that’s part of the fun! It’s all about creative surprises and letting the AI do its thing.
Try it now: DawnC/PawMatchAI
If this new feature made you smile, a ❤️ for this space would mean a lot.
#AIArt #StyleTransfer #StableDiffusion #ComputerVision #MachineLearning #DeepLearning
I’m excited to introduce a brand-new creative feature — Dog Style Transfer is now live on PawMatchAI!
Just upload your dog’s photo and transform it into 5 artistic styles:
🌸 Japanese Anime
📚 Classic Cartoon
🖼️ Oil Painting
🎨 Watercolor
🌆 Cyberpunk
All powered by Stable Diffusion and enhanced with smart prompt tuning to preserve your dog’s unique traits and breed identity , so the artwork stays true to your furry friend.
Whether you're creating a custom portrait or just having fun, this feature brings your pet photos to life in completely new ways.
And here’s a little secret: although it’s designed with dogs in mind, it actually works on any photo — cats, plush toys, even humans. Feel free to experiment!
Results may not always be perfectly accurate, sometimes your photo might come back looking a little different, or even beyond your imagination. But that’s part of the fun! It’s all about creative surprises and letting the AI do its thing.
Try it now: DawnC/PawMatchAI
If this new feature made you smile, a ❤️ for this space would mean a lot.
#AIArt #StyleTransfer #StableDiffusion #ComputerVision #MachineLearning #DeepLearning
-
- 2 replies
posted
an
update
4 months ago
Post
2387
🌐 PawMatchAI Update: Smarter Visualization with Radar Charts! 🐾
I’ve just added a new feature to the project that bridges the gap between breed recognition and real world decision-making:
👉 Radar charts for lifestyle-based breed insights.
🎯 Why This Matters
Choosing the right dog isn’t just about knowing the breed , it’s about how that breed fits into your lifestyle.
To make this intuitive, each breed now comes with a six-dimensional radar chart that reflects:
- Space Requirements
- Exercise Needs
- Grooming Level
- Owner Experience
- Health Considerations
- Noise Behavior
Users can also compare two breeds side-by-side using radar and bar charts — perfect for making thoughtful, informed choices.
💡 What’s Behind It?
All visualizations are directly powered by the same internal database used by the recommendation engine, ensuring consistent, explainable results.
🐶 Try It Out
Whether you're a first-time dog owner or a seasoned canine lover, this update makes it easier than ever to match with your ideal companion.
👉 Explore it here:
🔗 DawnC/PawMatchAI
Thanks for all the support so far, if you find this project helpful or interesting, feel free to leave a ❤️ on the Hugging Face Space!
#AI #ComputerVision #DataVisualization #DeepLearning #DataScience
I’ve just added a new feature to the project that bridges the gap between breed recognition and real world decision-making:
👉 Radar charts for lifestyle-based breed insights.
🎯 Why This Matters
Choosing the right dog isn’t just about knowing the breed , it’s about how that breed fits into your lifestyle.
To make this intuitive, each breed now comes with a six-dimensional radar chart that reflects:
- Space Requirements
- Exercise Needs
- Grooming Level
- Owner Experience
- Health Considerations
- Noise Behavior
Users can also compare two breeds side-by-side using radar and bar charts — perfect for making thoughtful, informed choices.
💡 What’s Behind It?
All visualizations are directly powered by the same internal database used by the recommendation engine, ensuring consistent, explainable results.
🐶 Try It Out
Whether you're a first-time dog owner or a seasoned canine lover, this update makes it easier than ever to match with your ideal companion.
👉 Explore it here:
🔗 DawnC/PawMatchAI
Thanks for all the support so far, if you find this project helpful or interesting, feel free to leave a ❤️ on the Hugging Face Space!
#AI #ComputerVision #DataVisualization #DeepLearning #DataScience
- 4 replies
replied to
their
post
6 months ago
Thank you for your positive feedback and your offer to help with marketing. I truly appreciate the interest in this project!
Naturally, it’s great if more people get to know about this project, as it helps showcase my work. However, at this stage, I don’t have any plans to monetize it. My primary focus remains on career transition into the tech industry, and this project serves as a portfolio piece demonstrating my technical skills.
That said, I’m always open to technical discussions and improvements that could enhance its educational value. If there’s something particularly interesting, I might consider exploring it in the future.
Thanks again for your support and for understanding my current priorities!
replied to
their
post
6 months ago
Thank you for the thorough review of the license changes. After careful consideration, I have decided to fully implement the Apache License 2.0. This update ensures that the project adheres to widely accepted open-source licensing standards while maintaining proper attribution.
The project is now fully under the standard Apache 2.0 license, meaning:
- Full redistribution rights are granted, both for commercial and non-commercial use
- Attribution requirements are clearly defined as per the Apache 2.0 license
- Patent rights are explicitly granted
- No additional restrictions beyond the standard Apache 2.0 terms
I have removed any previous mentions of "personal use" to align with Apache 2.0's unrestricted usage model. The license now fully complies with the standard terms without any additional conditions.
replied to
their
post
6 months ago
Thank you for your valuable insights and suggestions regarding the licensing issues. After careful consideration, I have updated the project's licensing terms to better reflect both the open-source community's needs and the project's purpose.
Initially, I chose a more restrictive license (CC BY-NC-ND 4.0) to protect the project's integrity as part of my career transition portfolio. However, after reflecting on the practical aspects of software licensing and the spirit of open-source collaboration, I decided to revise the terms.
The new license now:
- Allows broader usage, including potential commercial applications
- Maintains core attribution requirements to recognize original contributions
- Simplifies usage while preserving the project's value as a portfolio piece
This update strikes a balance between open-source principles and ensuring proper credit for the work. While it removes previous restrictions, it still requires attribution to acknowledge the original author.
I appreciate your thoughts on the challenges of enforcing restrictions in the software domain. With this new approach, I aim to focus more on proper attribution rather than limiting usage, which I believe aligns better with both community values and the project's intent.
Thanks again for your feedback—it helped me think through this issue more thoroughly.
replied to
their
post
6 months ago
Thank you for your interest in my project and for sharing the Free Software Foundation's philosophy. I appreciate your question about the licensing.
I would like to clarify that my project uses the CC BY-NC-ND 4.0 (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International) license. This license allows:
- Viewing and learning from the project content
- Sharing the original content (with attribution to me as the original author)
- Use for personal study and academic research purposes
However, it specifically prohibits:
- Commercial use
- Distribution of modified versions
- Creation of derivative works
This differs from traditional free software licenses as it provides more protection for intellectual property rights while still supporting educational and research purposes.