Initial Seg Data Server Net platform

README.md

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+# 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
+
+```text
+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
+
+```bash
+cd Seg_Data_Server_Net
+cp .env.example .env
+
+# Backend. The existing machine already has a seg_server env with FastAPI.
+conda run -n seg_server uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8000
+
+# 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:8000` by default.
+
+## 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`.
+
+```bash
+cd Seg_Data_Server_Net
+python scripts/sync_weights.py --mode copy --hash
+```
+
+For repository storage, use Git LFS or a Gitea release/package store:
+
+```bash
+git lfs install
+git lfs track "*.pt" "*.pth" "*.onnx" "*.engine"
+```
+
+If Git LFS is not available on the host or server, keep the copied weights on
+the deployment volume and commit only `weights/manifest.json`.
+
+## 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.
+