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FlashStudio

Interactive Training & Inference UI for FlashDet


Install

pip install flashstudio
pip install git+https://github.com/FlashVision/FlashDet.git

Quick Start

# Launch locally
flashstudio

# Or with Python
from flashstudio import launch
launch()

Google Colab

!pip install flashstudio
!pip install git+https://github.com/FlashVision/FlashDet.git

from flashstudio import launch
launch(ngrok_token="YOUR_TOKEN")

Get a free ngrok token at: https://dashboard.ngrok.com/get-started/your-authtoken

Features

  • Training — Launch and monitor FlashDet training with live loss curves
  • Inference — Run detection on images/video with 17 built-in solutions
  • Model Config — FlashDet (Pico → X) + YOLOv8/v9/v10/v11/YOLOX
  • Data — Upload datasets or download from FlashDet registry
  • Export — ONNX export with FP16 support
  • Dashboard — Project overview with recent runs and metrics

Supported Models

Model Params Use Case
FlashDet-Pico ~298K Edge / MCU
FlashDet-Nano ~790K Embedded / IoT
FlashDet-Small ~1.8M General purpose
FlashDet-Medium ~3.6M High accuracy
FlashDet-Large ~5.8M High accuracy
FlashDet-X ~9.0M Server / Max accuracy

Requirements

  • Python >= 3.10
  • FlashDet (for training/inference)
  • ngrok account (free, for Colab)
  • GPU recommended for training

License

Apache-2.0

Metadata

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