scorevision: push artifact

README.md

@@ -0,0 +1,76 @@
+---
+tags:
+- element_type:detect
+- model:yolov11-nano
+- object:vehicle
+manako:
+  description: YOLO11n vehicle detector for CCTV surveillance
+  input_payload:
+  - name: frame
+    type: image
+    description: RGB frame
+  output_payload:
+  - name: detections
+    type: detections
+    description: List of detections
+---
+
+# Score Vision SN44 — VehicleDetect Miner
+
+**Wallet:** LukeTao | **Hotkey:** default | **UID:** 128 | **Netuid:** 44
+
+## Model
+
+| Property | Value |
+|---|---|
+| Architecture | YOLO11-nano |
+| Input size | 640×640 |
+| Model file | `weights.onnx` |
+| Size | ~11 MB (well under 30 MB limit) |
+| Framework | ONNX Runtime (CUDA EP) |
+| mAP\@50 | **63.05%** (COCO val2017, vehicle classes) |
+
+## Classes
+
+| Output ID | Class | COCO Index |
+|---|---|---|
+| 0 | car | 2 |
+| 1 | bus | 5 |
+| 2 | truck | 7 |
+| 3 | motorcycle | 3 |
+
+## Performance
+
+Measured on RTX 4090, COCO val2017 images (640×640 letterbox):
+
+| Metric | Value | Target |
+|---|---|---|
+| Mean FPS (CUDA) | ~371 | ≥ 30 |
+| Mean FPS (CPU)  | ~34  | ≥ 30 |
+| P95 latency (CUDA) | 2.83 ms | < 50 ms |
+| Inference (GPU) | 2.70 ms | — |
+
+## Output Format
+
+```json
+[{
+  "x": 320.5,
+  "y": 240.1,
+  "width": 150.0,
+  "height": 90.0,
+  "confidence": 0.91,
+  "class_id": 0,
+  "class": "car"
+}]
+```
+
+## Files
+
+| File | Purpose |
+|---|---|
+| `weights.onnx` | ONNX model (YOLO11-nano, opset 12) |
+| `main.py` | Inference runner (reads class_names.txt automatically) |
+| `class_names.txt` | One class name per line |
+| `model_type.json` | Model metadata |
+| `pyproject.toml` | Python package dependencies |
+| `README.md` | This file |