Easy-to-manage touch screens
무료 오픈소스 터치스크린으로 USB 설정 가이드, 드래그‑드롭 편집기, 로컬 제어를 제공하며 Home Assistant용으로 제작되었습니다
FindFlower로 식물을 탐색하고 식별합니다. 기기 내 AI 분류와 방대한 식물 종 데이터베이스 제공
Explore and identify flora with FindFlower, an installable Progressive Web App featuring on-device AI classification and an extensive botanical species directory.
라이프스타일Python · JavaScriptGitHub @gsor56온라인운영자 미확인
써 보셨어요? 한 번만 눌러 주세요. 로그인은 필요 없어요.
아직 감지된 업데이트가 없습니다. GitHub 릴리스가 나오면 여기에 보입니다.
개인 계정의 공개 저장소 소유자는 GitHub로 로그인해 운영자를 확인하고 소개를 직접 관리할 수 있습니다. 관리자 승인은 필요하지 않습니다.
로그인한 뒤 확인 파일을 만들어 이 저장소의 기본 브랜치에 30분 안에 커밋해 주세요. 다른 브랜치나 포크의 파일은 인정하지 않습니다. 파일을 올린 뒤 아래에서 확인하면 관리자 승인 없이 관리할 수 있습니다. 자격을 유지하려면 파일을 남겨 두세요.
로그인이 필요하면 로그인 화면으로 이동합니다. 로그인 후 이곳으로 돌아와 원하는 확인 버튼을 다시 눌러 주세요.
프로젝트 폴더에서 /nomorevibe verify를 실행하면 소유권을 확인하고 소개를 직접 관리할 수 있습니다.
1분 만에 확인하기🌸 High-accuracy botanical vision pipeline — real-time species classification leveraging cloud-hosted ViT models, low-latency edge proxying, and client-side web acceleration.
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
Point your camera at a flower. Know what it is in seconds.
Flower identification for 116 species, powered by a fine-tuned Vision Transformer.
Ever seen a flower on a walk and had no idea what it was called?
Snap a photo, upload one, or paste an image link, and get a species name plus a
confidence score in a few seconds. No app to install, no account needed to try it.
📸 Snap or upload a photo of any flower
🔒 Sent over HTTPS via our inference proxy
🌼 ViT identifies it 116 species, top-5 scores
📚 Learn more pulled live from WikipediaREADME 전문 보기 ↗Flower identification for 116 species, powered by a fine-tuned Vision Transformer.
Ever seen a flower on a walk and had no idea what it was called?
Snap a photo, upload one, or paste an image link, and get a species name plus a confidence score in a few seconds. No app to install, no account needed to try it.
Identification runs server-side on a fine-tuned ViT-base. Your image is sent over HTTPS to do that, used only to run the prediction, and not stored — see the privacy page for specifics.
[!NOTE] Earlier betas ran a smaller CNN in-browser via TensorFlow.js, so images stayed on your device. The ViT is far more accurate but too large to ship to a browser, so inference moved to a server. The privacy tradeoff is deliberate and documented rather than glossed over.
browser ──image──▶ Cloudflare Worker ──image + X-Proxy-Secret──▶ ONNX backend
(origin allowlist, (FastAPI +
holds the secret) onnxruntime)
◀── { flower, confidence, top_k } ────────────────────────────────────┘
The Worker keeps the backend from being an open public API: it enforces an origin allowlist and attaches a shared secret the backend checks. Only labels and scores come back, so the weights never leave the container.
| Path | What lives there |
|---|---|
*.html, *.js, favicon.svg | The static frontend (GitHub Pages) |
space/ | FastAPI + onnxruntime inference server |
proxy/ | Cloudflare Worker that fronts the backend |
training/ | Kaggle training + ONNX conversion scripts |
tools/ | Build script for the Trefle species data |
Only the frontend files are published to the web root —
.github/workflows/pages.yml copies those into _site/ and deploys that, so the
training scripts stay in version control without being served.
Frontend only — it talks to the deployed Worker, so there's no build step:
git clone https://github.com/gsor56/findflower.git
cd findflower
python -m http.server 8000 # http://localhost:8000 is on the CORS allowlist
The backend and Worker each have their own setup notes in
space/README.md and proxy/README.md.
Got a misidentification? Hit "No, incorrect" right on the try page — every bit of feedback helps retrain the model.
This is a solo, student-built project — feedback, bug reports, and suggestions are genuinely welcome. Open an issue or reach out via the contact page.
Released under the MIT License — free to use, modify, and learn from.