Seg Data Server Net

Modular web control plane for the existing Seg image segmentation workspace.

The platform keeps the current training and analysis scripts as the compute core, then adds:

• a FastAPI backend for catalog discovery, job orchestration, logs, results, GPU status, and weight management;
• a React/Vite frontend for launching jobs and inspecting progress;
• a unified weights/ area with a generated manifest for .pt, .pth, .onnx, and .engine assets.

Layout

Seg_Data_Server_Net/
  backend/            FastAPI API, job runner, module wrappers
  frontend/           React + Vite operator UI
  scripts/            helper scripts for running services and syncing weights
  weights/            copied model weights and manifest.json

Quick Start

cd Seg_Data_Server_Net
cp .env.example .env

# Backend. The deployment env is seg_smp so the API and task wrappers share
# the same segmentation dependency stack.
conda run -n seg_smp uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8010

# Frontend.
cd frontend
npm install
npm run dev -- --host 0.0.0.0

Open the Vite URL shown in the terminal. The frontend expects the backend at http://localhost:8010 by default.

The web UI includes a dataset bench for creating upload workspaces, uploading images/labels/masks, and jumping into the existing rename, PNG conversion, resize, pair-check, label rebuild, transparent overlay, stitch, and video-frame jobs. Segmentation previews, YOLO heatmaps, and loss/metric artifacts are grouped on the results dashboard.

Weight Sync

The current workspace contains tens of GB of pretrained and trained weights. They are copied into weights/files/<original-relative-path> and indexed in weights/manifest.json.

cd Seg_Data_Server_Net
python scripts/sync_weights.py --mode copy --hash

Weights must remain local to the deployment machine. Do not push .pt, .pth, .onnx, .engine, or weights/files/ into Gitea. The repository stores only code, weights/manifest.json, and helper scripts. Before pushing, run:

scripts/check_no_weight_git.sh

If a deployment machine needs weights, run the sync command locally on that machine after cloning the code.

Job Types

The backend exposes all current Seg capabilities as job types. Examples:

• dataset.rename, dataset.resize, dataset.pair, dataset.rebuild_labels, dataset.stack, dataset.stitch, dataset.video_frames
• segmodel.train, segmodel.batch_train, segmodel.predict, segmodel.batch_predict, segmodel.flops, segmodel.raw_mask_check
• yolo.train, yolo.batch_train, yolo.predict, yolo.batch_predict, yolo.heatmap, yolo.compare, yolo.raw_mask_check, yolo.video_visible
• mmseg.generate_data, mmseg.generate_alg, mmseg.train, mmseg.metrics, mmseg.flops_fps, mmseg.draw, mmseg.extract_loss_miou
• analysis.all, system.backup, mock.echo

Use GET /api/catalog to inspect supported models, algorithms, datasets, and task types discovered from the existing Seg/ workspace.

Agents

Run the local evaluation and validation agents before publishing changes:

PYTHONPATH=backend conda run -n seg_smp python scripts/run_agents.py --build

The validation agent checks catalog coverage, the new seg_smp env, GPU visibility, no-weight Git safety, backend tests, frontend build, and live backend/frontend endpoints when the services are running.