From 2c9cde84cab322378a422a2531fc0d1d4e0a6b73 Mon Sep 17 00:00:00 2001
From: kassam <kassam@yslootahtech.com>
Date: Mon, 20 Apr 2026 00:02:37 +0400
Subject: [PATCH] Update 2026-04-20 00:02:36

---
 README.md | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)

diff --git a/README.md b/README.md
index 0ab898a..c826906 100644
--- a/README.md
+++ b/README.md
@@ -49,3 +49,53 @@ python -m saqr.robot.bridge --iface eth0 --source realsense --headless
 - [docs/DEPLOY.md](docs/DEPLOY.md) — full deploy + robot setup.
 - [docs/start.md](docs/start.md) — systemd auto-start workflow.
 - [docs/use_case_catalogue.pdf](docs/use_case_catalogue.pdf) — PPE use-case spec.
+
+## Data & Models
+
+The `data/` and `runtime/` directories are excluded from git (too large).
+Download them separately before training or running inference.
+
+### `data/` — dataset and pre-trained weights
+
+Expected contents:
+
+```
+data/
+  dataset/
+    train/{images,labels}/
+    valid/{images,labels}/
+    test/{images,labels}/
+    data.yaml
+  models/
+    saqr_best.pt       # Saqr YOLO11n fine-tuned on PPE
+    saqr_last.pt
+    yolo11n.pt         # base YOLO11n
+    yolo26n.pt         # base YOLO26n
+```
+
+Download:
+
+- **Dataset** (PPE, Roboflow): [testcasque/ppe-detection-qlq3d](https://universe.roboflow.com/testcasque/ppe-detection-qlq3d)
+  Open the Roboflow link → *Download Dataset* → format **YOLOv11** → unzip into `data/dataset/`.
+- **Base YOLO weights**: [Ultralytics assets releases](https://github.com/ultralytics/assets/releases)
+  Grab `yolo11n.pt` (and optionally `yolo26n.pt`) into `data/models/`.
+- **Saqr fine-tuned weights** (`saqr_best.pt`, `saqr_last.pt`):
+  Produced by training — see "Training" below. Or request from the maintainer.
+
+Place everything under `data/` so the tree matches above.
+
+### `runtime/` — training output (optional)
+
+Auto-generated when you run training. Not required for inference.
+Contains confusion matrices, PR curves, batch previews, and the raw weights
+under `runtime/runs/train/saqr_det/weights/`.
+
+### Training
+
+```bash
+# after placing the dataset in data/dataset/ and base weights in data/models/
+python -m saqr.apps.train_cli --data data/dataset/data.yaml --weights data/models/yolo11n.pt
+```
+
+Outputs land in `runtime/runs/train/saqr_det/`. Copy the best checkpoint to
+`data/models/saqr_best.pt` to use it at inference time.