scorevision: push artifact

README.md

@@ -2,75 +2,41 @@
 tags:
 - element_type:detect
 - model:yolov11-nano
-- object:vehicle
+- object:person
 manako:
-  description: YOLO11n vehicle detector for CCTV surveillance
+  description: >
+    YOLOv11-nano fine-tuned for ground-level CCTV person detection on SN44.
+    Trained on CrowdHuman (15k, dense crowds) + BDD100K street pedestrians.
+    Conf threshold raised to 0.35 to minimise false positives.
+  source: meaculpitt/Detect-Person
+  prompt_hints: null
   input_payload:
   - name: frame
     type: image
-    description: RGB frame
+    description: RGB frame (ground-level CCTV)
   output_payload:
   - name: detections
     type: detections
-    description: List of detections
+    description: Bounding boxes for detected persons
+  evaluation_score: 0.5563
+  last_benchmark:
+    type: coco_val2017
+    ran_at: '2026-03-25T02:58:57+00:00'
+    result_path: null
 ---
 
-# Score Vision SN44 — VehicleDetect Miner
+# Detect-Person — SN44
 
-**Wallet:** LukeTao | **Hotkey:** default | **UID:** 128 | **Netuid:** 44
+YOLOv11-nano fine-tuned for ground-level CCTV person detection.
 
-## Model
+| Metric | Value |
+|--------|-------|
+| mAP@50 (COCO val2017) | 55.63% |
+| Precision (conf=0.35) | 56.86% |
+| Recall | 50.67% |
+| Baseline to beat | 37.55% |
+| Model size | 5.6 MB |
+| Input size | 1280×1280 |
 
-| 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 |
+**Training data**: CrowdHuman (15k) + BDD100K (3.2k pedestrians)
+**Validation**: COCO val2017 persons (2,693 images)