scorevision: push artifact

README.md

@@ -1,13 +1,14 @@
 ---
 tags:
 - element_type:detect
-- model:yolov11-small
-- object:vehicle
+- model:yolov11-nano
+- object:person
 manako:
   description: >
-    YOLO11s vehicle detector fine-tuned on COCO vehicles + BDD100K + VisDrone.
-    FP16 ONNX, 1280x1280 input. Trained R6: 59,870 images, 50 epochs.
-  source: meaculpitt/Detect-Vehicle
+    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
@@ -16,32 +17,26 @@ manako:
   output_payload:
   - name: detections
     type: detections
-    description: Bounding boxes for detected vehicles
-  evaluation_score: 0.7701
+    description: Bounding boxes for detected persons
+  evaluation_score: 0.5563
   last_benchmark:
-    type: visdrone_val
-    ran_at: 2026-03-25T17:34:00+00:00
+    type: coco_val2017
+    ran_at: '2026-03-25T02:58:57+00:00'
     result_path: null
 ---
 
-# Detect-Vehicle — SN44
+# Detect-Person — SN44
 
-YOLO11s fine-tuned for vehicle detection (car, bus, truck, motorcycle).
+YOLOv11-nano fine-tuned for ground-level CCTV person detection.
 
 | Metric | Value |
 |--------|-------|
-| mAP@50 | 77.01% |
-| Model | YOLO11s (FP16 ONNX) |
-| Input size | 1280x1280 |
-| Model size | 19.2 MB |
-| Training data | COCO vehicles + BDD100K + VisDrone (59,870 images) |
-| Baseline to beat | 40.72% |
+| 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 |
 
-## Classes
-
-| Output ID | Class |
-|-----------|-------|
-| 0 | car |
-| 1 | bus |
-| 2 | truck |
-| 3 | motorcycle |
+**Training data**: CrowdHuman (15k) + BDD100K (3.2k pedestrians)
+**Validation**: COCO val2017 persons (2,693 images)

class_names.txt

@@ -1,80 +1 @@
 person
-bicycle
-car
-motorcycle
-airplane
-bus
-train
-truck
-boat
-traffic light
-fire hydrant
-stop sign
-parking meter
-bench
-bird
-cat
-dog
-horse
-sheep
-cow
-elephant
-bear
-zebra
-giraffe
-backpack
-umbrella
-handbag
-tie
-suitcase
-frisbee
-skis
-snowboard
-sports ball
-kite
-baseball bat
-baseball glove
-skateboard
-surfboard
-tennis racket
-bottle
-wine glass
-cup
-fork
-knife
-spoon
-bowl
-banana
-apple
-sandwich
-orange
-broccoli
-carrot
-hot dog
-pizza
-donut
-cake
-chair
-couch
-potted plant
-bed
-dining table
-toilet
-tv
-laptop
-mouse
-remote
-keyboard
-cell phone
-microwave
-oven
-toaster
-sink
-refrigerator
-book
-clock
-vase
-scissors
-teddy bear
-hair drier
-toothbrush

miner.py

@@ -1,12 +1,7 @@
 """
-Score Vision SN44 — VehicleDetect miner. v3 (2026-03-26).
-TTA (3 augmentations) + WBF fusion. Letterbox preprocessing.
-
-Model: YOLO11s ONNX, 4 classes trained as:
-  0 = car, 1 = bus, 2 = truck, 3 = motorcycle
-
-Official submission order (remapped in MODEL_TO_OUT):
-  0 = bus, 1 = car, 2 = truck, 3 = motorcycle
+Score Vision SN44 — DetectPerson miner. v2 (2026-03-26).
+TTA (3 augmentations) + WBF fusion. Stretch resize preprocessing.
+Single class: person (cls_id=0).
 """
 
 from pathlib import Path
@@ -19,15 +14,6 @@ from numpy import ndarray
 from pydantic import BaseModel
 from ensemble_boxes import weighted_boxes_fusion
 
-
-# ── Model class index → submission class index ───────────────────────────────
-MODEL_TO_OUT: dict[int, int] = {0: 1, 1: 0, 2: 2, 3: 3}
-OUT_TO_MODEL: dict[int, int] = {v: k for k, v in MODEL_TO_OUT.items()}
-OUT_NAMES = ["bus", "car", "truck", "motorcycle"]
-
-IMG_SIZE = 1280
-CONF_THRESH = 0.55
-IOU_THRESH = 0.45
 WBF_IOU_THR = 0.55
 WBF_SKIP_THR = 0.0001
 TTA_SCALE = 1.2
@@ -51,78 +37,94 @@ class TVFrameResult(BaseModel):
 class Miner:
     def __init__(self, path_hf_repo: Path) -> None:
         self.path_hf_repo = path_hf_repo
+        self.class_names = ['person']
         self.session = ort.InferenceSession(
             str(path_hf_repo / "weights.onnx"),
             providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
         )
         self.input_name = self.session.get_inputs()[0].name
-        self.conf_threshold = CONF_THRESH
-        self.iou_threshold = IOU_THRESH
+        input_shape = self.session.get_inputs()[0].shape
+        self.input_h = int(input_shape[2])
+        self.input_w = int(input_shape[3])
+        self.conf_threshold = 0.50
+        self.iou_threshold = 0.45
 
     def __repr__(self) -> str:
-        return f"VehicleDetect Miner TTA+WBF session={type(self.session).__name__}"
-
-    def _letterbox(self, img: ndarray) -> tuple[np.ndarray, float, int, int]:
-        h, w = img.shape[:2]
-        r = min(IMG_SIZE / h, IMG_SIZE / w)
-        new_w, new_h = int(round(w * r)), int(round(h * r))
-        img_r = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
-        dw, dh = IMG_SIZE - new_w, IMG_SIZE - new_h
-        pad_l, pad_t = dw // 2, dh // 2
-        img_p = cv2.copyMakeBorder(
-            img_r, pad_t, dh - pad_t, pad_l, dw - pad_l,
-            cv2.BORDER_CONSTANT, value=(114, 114, 114),
-        )
-        return img_p, r, pad_l, pad_t
-
-    def _preprocess(self, image_bgr: ndarray) -> tuple[np.ndarray, float, int, int]:
-        img_p, ratio, pad_l, pad_t = self._letterbox(image_bgr)
-        img_rgb = cv2.cvtColor(img_p, cv2.COLOR_BGR2RGB)
-        inp = img_rgb.astype(np.float32) / 255.0
-        inp = np.ascontiguousarray(inp.transpose(2, 0, 1)[np.newaxis])
-        return inp, ratio, pad_l, pad_t
-
-    def _decode_raw(self, raw: np.ndarray, ratio: float, pad_l: int, pad_t: int,
-                    orig_w: int, orig_h: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        return f"DetectPerson Miner TTA+WBF session={type(self.session).__name__}"
+
+    def _preprocess(self, image_bgr: ndarray) -> tuple[np.ndarray, tuple[int, int]]:
+        h, w = image_bgr.shape[:2]
+        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+        resized = cv2.resize(rgb, (self.input_w, self.input_h))
+        x = resized.astype(np.float32) / 255.0
+        x = np.transpose(x, (2, 0, 1))[None, ...]
+        return x, (h, w)
+
+    def _decode_raw(self, raw: np.ndarray, orig_h: int, orig_w: int
+                    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Decode ONNX output to (boxes_xyxy, confs, cls_ids) in original image coords."""
         pred = raw[0]
+        if pred.ndim != 2:
+            return np.empty((0, 4)), np.empty(0), np.empty(0, dtype=int)
         if pred.shape[0] < pred.shape[1]:
-            pred = pred.T
+            pred = pred.transpose(1, 0)
+
+        if pred.shape[1] < 5:
+            return np.empty((0, 4)), np.empty(0), np.empty(0, dtype=int)
 
-        bboxes_cx = pred[:, :4]
+        boxes = pred[:, :4]
         cls_scores = pred[:, 4:]
+        if cls_scores.shape[1] == 0:
+            return np.empty((0, 4)), np.empty(0), np.empty(0, dtype=int)
 
         cls_ids = np.argmax(cls_scores, axis=1)
         confs = np.max(cls_scores, axis=1)
-        mask = confs >= self.conf_threshold
+        keep = confs >= self.conf_threshold
 
-        if not mask.any():
-            return np.empty((0, 4)), np.empty(0), np.empty(0, dtype=int)
+        boxes = boxes[keep]
+        confs = confs[keep]
+        cls_ids = cls_ids[keep]
 
-        bboxes_cx = bboxes_cx[mask]
-        confs = confs[mask]
-        cls_ids = cls_ids[mask]
+        if boxes.shape[0] == 0:
+            return np.empty((0, 4)), np.empty(0), np.empty(0, dtype=int)
 
-        cx, cy, bw, bh = bboxes_cx[:, 0], bboxes_cx[:, 1], bboxes_cx[:, 2], bboxes_cx[:, 3]
-        lx1 = cx - bw / 2
-        ly1 = cy - bh / 2
-        lx2 = cx + bw / 2
-        ly2 = cy + bh / 2
+        sx = orig_w / float(self.input_w)
+        sy = orig_h / float(self.input_h)
 
-        x1 = np.clip((lx1 - pad_l) / ratio, 0, orig_w)
-        y1 = np.clip((ly1 - pad_t) / ratio, 0, orig_h)
-        x2 = np.clip((lx2 - pad_l) / ratio, 0, orig_w)
-        y2 = np.clip((ly2 - pad_t) / ratio, 0, orig_h)
-        boxes = np.stack([x1, y1, x2, y2], axis=1)
+        cx, cy, bw, bh = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
+        x1 = np.clip((cx - bw / 2) * sx, 0, orig_w)
+        y1 = np.clip((cy - bh / 2) * sy, 0, orig_h)
+        x2 = np.clip((cx + bw / 2) * sx, 0, orig_w)
+        y2 = np.clip((cy + bh / 2) * sy, 0, orig_h)
+        out_boxes = np.stack([x1, y1, x2, y2], axis=1)
 
-        return boxes, confs, cls_ids
+        return out_boxes, confs, cls_ids
 
     def _run_single_pass(self, image_bgr: ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Run one inference pass, return (boxes_xyxy, confs, cls_ids) in original coords."""
         orig_h, orig_w = image_bgr.shape[:2]
-        inp, ratio, pad_l, pad_t = self._preprocess(image_bgr)
+        inp, _ = self._preprocess(image_bgr)
         raw = self.session.run(None, {self.input_name: inp})[0]
-        return self._decode_raw(raw, ratio, pad_l, pad_t, orig_w, orig_h)
+        return self._decode_raw(raw, orig_h, orig_w)
+
+    def _nms_dets(self, boxes: np.ndarray, confs: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        """Simple NMS on boxes+confs."""
+        if len(boxes) == 0:
+            return boxes, confs
+        x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
+        areas = (x2 - x1) * (y2 - y1)
+        order = confs.argsort()[::-1]
+        keep = []
+        while len(order):
+            i = order[0]
+            keep.append(int(i))
+            xx1 = np.maximum(x1[i], x1[order[1:]])
+            yy1 = np.maximum(y1[i], y1[order[1:]])
+            xx2 = np.minimum(x2[i], x2[order[1:]])
+            yy2 = np.minimum(y2[i], y2[order[1:]])
+            inter = np.maximum(0, xx2 - xx1) * np.maximum(0, yy2 - yy1)
+            iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-7)
+            order = order[1:][iou <= self.iou_threshold]
+        return boxes[keep], confs[keep]
 
     def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
         orig_h, orig_w = image_bgr.shape[:2]
@@ -134,55 +136,47 @@ class Miner:
         # ── TTA pass 1: original ──
         boxes, confs, cls_ids = self._run_single_pass(image_bgr)
         if len(boxes):
-            # Remap to output class IDs for WBF
-            out_cls = np.array([MODEL_TO_OUT[int(c)] for c in cls_ids])
-            # Normalize to [0,1]
             norm_boxes = boxes.copy()
             norm_boxes[:, [0, 2]] /= orig_w
             norm_boxes[:, [1, 3]] /= orig_h
             norm_boxes = np.clip(norm_boxes, 0, 1)
             all_boxes_list.append(norm_boxes)
             all_scores_list.append(confs)
-            all_labels_list.append(out_cls)
+            all_labels_list.append(cls_ids.astype(float))
 
         # ── TTA pass 2: horizontal flip ──
         flipped = cv2.flip(image_bgr, 1)
         boxes_f, confs_f, cls_ids_f = self._run_single_pass(flipped)
         if len(boxes_f):
-            # Flip x coords back
             boxes_f[:, 0], boxes_f[:, 2] = orig_w - boxes_f[:, 2], orig_w - boxes_f[:, 0]
-            out_cls_f = np.array([MODEL_TO_OUT[int(c)] for c in cls_ids_f])
             norm_boxes_f = boxes_f.copy()
             norm_boxes_f[:, [0, 2]] /= orig_w
             norm_boxes_f[:, [1, 3]] /= orig_h
             norm_boxes_f = np.clip(norm_boxes_f, 0, 1)
             all_boxes_list.append(norm_boxes_f)
             all_scores_list.append(confs_f)
-            all_labels_list.append(out_cls_f)
+            all_labels_list.append(cls_ids_f.astype(float))
 
-        # ── TTA pass 3: scale 1.2x (center crop to original size) ──
+        # ── TTA pass 3: scale 1.2x center crop ──
         scaled_h, scaled_w = int(orig_h * TTA_SCALE), int(orig_w * TTA_SCALE)
         scaled = cv2.resize(image_bgr, (scaled_w, scaled_h), interpolation=cv2.INTER_LINEAR)
-        # Center crop back to original size
         y_off = (scaled_h - orig_h) // 2
         x_off = (scaled_w - orig_w) // 2
         cropped = scaled[y_off:y_off + orig_h, x_off:x_off + orig_w]
         boxes_s, confs_s, cls_ids_s = self._run_single_pass(cropped)
         if len(boxes_s):
-            # Map cropped coords back to original: offset + scale
             boxes_s[:, 0] = (boxes_s[:, 0] + x_off) / TTA_SCALE
             boxes_s[:, 1] = (boxes_s[:, 1] + y_off) / TTA_SCALE
             boxes_s[:, 2] = (boxes_s[:, 2] + x_off) / TTA_SCALE
             boxes_s[:, 3] = (boxes_s[:, 3] + y_off) / TTA_SCALE
             boxes_s = np.clip(boxes_s, 0, [[orig_w, orig_h, orig_w, orig_h]])
-            out_cls_s = np.array([MODEL_TO_OUT[int(c)] for c in cls_ids_s])
             norm_boxes_s = boxes_s.copy()
             norm_boxes_s[:, [0, 2]] /= orig_w
             norm_boxes_s[:, [1, 3]] /= orig_h
             norm_boxes_s = np.clip(norm_boxes_s, 0, 1)
             all_boxes_list.append(norm_boxes_s)
             all_scores_list.append(confs_s)
-            all_labels_list.append(out_cls_s)
+            all_labels_list.append(cls_ids_s.astype(float))
 
         if not all_boxes_list:
             return []
@@ -223,9 +217,11 @@ class Miner:
         for idx, image in enumerate(batch_images):
             boxes = self._infer_single(image)
             keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
-            results.append(TVFrameResult(
-                frame_id=offset + idx,
-                boxes=boxes,
-                keypoints=keypoints,
-            ))
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + idx,
+                    boxes=boxes,
+                    keypoints=keypoints,
+                )
+            )
         return results

model_type.json

@@ -1 +1 @@
-{"task_type": "object-detection", "model_type": "yolov11-small", "deploy": "2026-03-26T07:43Z"}
+{"task_type": "object-detection", "model_type": "yolov11-nano", "deploy": "2026-03-26T07:46Z"}

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e3916408ec21f8c94358c18914f922814770b78557e52fe17ff7a9ee74339a5a
-size 19272252
+oid sha256:f32ed65b9024a69693f675d494c7fc813a964766c54b241464a463377342da60
+size 5607862