August brought a major milestone: the Loomi Clothing Detection API demo is finally live on Hugging Face Spaces. This isn't the full feature yet, but it's a working proof-of-concept that shows what's possible. After months of planning and development, seeing this actually work feels like a huge step forward.
The demo can detect 18 different clothing categories using a pre-trained Segformer B2 model, automatically remove backgrounds from clothing photos, and extract dominant colors. It's exactly the kind of AI-powered wardrobe analysis I've been planning since day one. The model is surprisingly accurate, even with the free tier constraints.
I went with FastAPI for the backend because it's incredibly fast and has built-in async support. The automatic API documentation is a lifesaver when you're iterating quickly on ML models. The demo runs smoothly on Hugging Face's free tier thanks to some smart CPU threading and efficient model loading.
The biggest challenge was working around the 1-request-at-a-time limitation. I built a two-stage caching system: first request detects clothing and caches the results, subsequent requests analyze the cached data instantly. It's not perfect, but it makes the demo feel responsive even with the constraints.
What's next? This demo will guide the development of the actual automatic background removal and clothing categorization features. Users will eventually be able to snap a photo of any clothing item and get instant AI analysis. For now, the demo is live and ready for testing. Drop a clothing photo and see what happens.
We're moving from a simple digital closet to something that can actually understand what's in your wardrobe. The AI integration opens up an ocean of possibilities.
— The Loomi Team