PawMatchAI โ Now with SBERT-Powered Recommendations! ๐ถโจ
โญ๏ธ NEW: Description-based recommendations are here! Just type in your lifestyle or preferences (e.g. โI live in an apartment and want a quiet dogโ), and PawMatchAI uses SBERT semantic embeddings to understand your needs and suggest compatible breeds.
What can PawMatchAI do today? ๐ธ Upload a photo to identify your dog from 124 breeds with detailed info. โ๏ธ Compare two breeds side-by-side, from grooming needs to health insights. ๐ Visualize breed traits with radar and comparison charts. ๐จ Try Style Transfer to turn your dogโs photo into anime, watercolor, cyberpunk, and more.
Whatโs next? ๐ฏ More fine-tuned recommendations. ๐ฑ Mobile-friendly deployment. ๐พ Expansion to additional species.
My goal: To make breed discovery not only accurate but also interactive and fun โ combining computer vision, semantic understanding, and creativity to help people find their perfect companion.
Okay this is insane... WebGPU-accelerated semantic video tracking, powered by DINOv3 and Transformers.js! ๐คฏ Demo (+ source code): webml-community/DINOv3-video-tracking
This will revolutionize AI-powered video editors... which can now run 100% locally in your browser, no server inference required (costs $0)! ๐
How does it work? ๐ค 1๏ธโฃ Generate and cache image features for each frame 2๏ธโฃ Create a list of embeddings for selected patch(es) 3๏ธโฃ Compute cosine similarity between each patch and the selected patch(es) 4๏ธโฃ Highlight those whose score is above some threshold
... et voilร ! ๐ฅณ
You can also make selections across frames to improve temporal consistency! This is super useful if the object changes its appearance slightly throughout the video.