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

@@ -1,13 +1,7 @@
 """
-Score Vision SN44 — VehicleDetect miner v7 (2026-03-27).
-TTA (2-pass: original + hflip) + inline WBF. Per-class conf thresholds.
-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 v6 (2026-03-27).
+TTA (2-pass: original + hflip) + inline WBF. Stretch resize preprocessing.
+Single class: person (cls_id=0).
 """
 
 from pathlib import Path
@@ -19,93 +13,75 @@ import onnxruntime as ort
 from numpy import ndarray
 from pydantic import BaseModel
 
-MODEL_TO_OUT: dict[int, int] = {0: 1, 1: 0, 2: 2, 3: 3}
-OUT_NAMES = ["bus", "car", "truck", "motorcycle"]
-NUM_CLASSES = 4
-
-IMG_SIZE = 1280
-# Per-class confidence thresholds (output class IDs):
-#   0=bus, 1=car, 2=truck, 3=motorcycle
-CONF_PER_CLASS = {0: 0.33, 1: 0.50, 2: 0.40, 3: 0.36}
-CONF_THRESH_DEFAULT = 0.35  # fallback
+CONF_THRESH = 0.35
 TTA_CONF_THRESH = 0.25
 IOU_THRESH = 0.45
-WBF_IOU_THR = 0.55
+WBF_IOU_THR = 0.45
 WBF_SKIP_THR = 0.0001
 
 
 def _wbf(boxes_list: list[np.ndarray], scores_list: list[np.ndarray],
-         labels_list: list[np.ndarray], iou_thr: float = 0.55,
-         skip_box_thr: float = 0.0001) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Weighted Boxes Fusion (inline, no external dep). Boxes in [0,1] normalized coords."""
+         iou_thr: float = 0.45, skip_box_thr: float = 0.0001
+         ) -> tuple[np.ndarray, np.ndarray]:
+    """Weighted Boxes Fusion for single-class detection. Boxes in [0,1] normalized coords."""
     if not boxes_list:
-        return np.empty((0, 4)), np.empty(0), np.empty(0)
+        return np.empty((0, 4)), np.empty(0)
 
-    all_boxes, all_scores, all_labels = [], [], []
-    for model_idx, (bx, sc, lb) in enumerate(zip(boxes_list, scores_list, labels_list)):
+    all_boxes, all_scores = [], []
+    for bx, sc in zip(boxes_list, scores_list):
         for i in range(len(bx)):
             if sc[i] < skip_box_thr:
                 continue
             all_boxes.append(bx[i])
             all_scores.append(sc[i])
-            all_labels.append(int(lb[i]))
 
     if not all_boxes:
-        return np.empty((0, 4)), np.empty(0), np.empty(0)
+        return np.empty((0, 4)), np.empty(0)
 
     all_boxes = np.array(all_boxes)
     all_scores = np.array(all_scores)
-    all_labels = np.array(all_labels, dtype=int)
-
-    fused_boxes, fused_scores, fused_labels = [], [], []
-
-    for cls in np.unique(all_labels):
-        cls_mask = all_labels == cls
-        cls_boxes = all_boxes[cls_mask]
-        cls_scores = all_scores[cls_mask]
-
-        order = cls_scores.argsort()[::-1]
-        cls_boxes = cls_boxes[order]
-        cls_scores = cls_scores[order]
-
-        clusters: list[list[int]] = []
-        cluster_boxes: list[np.ndarray] = []
-
-        for i in range(len(cls_boxes)):
-            matched = -1
-            best_iou = iou_thr
-            for c_idx, c_box in enumerate(cluster_boxes):
-                xx1 = max(cls_boxes[i, 0], c_box[0])
-                yy1 = max(cls_boxes[i, 1], c_box[1])
-                xx2 = min(cls_boxes[i, 2], c_box[2])
-                yy2 = min(cls_boxes[i, 3], c_box[3])
-                inter = max(0, xx2 - xx1) * max(0, yy2 - yy1)
-                a1 = (cls_boxes[i, 2] - cls_boxes[i, 0]) * (cls_boxes[i, 3] - cls_boxes[i, 1])
-                a2 = (c_box[2] - c_box[0]) * (c_box[3] - c_box[1])
-                iou = inter / (a1 + a2 - inter + 1e-9)
-                if iou > best_iou:
-                    best_iou = iou
-                    matched = c_idx
-            if matched >= 0:
-                clusters[matched].append(i)
-                idxs = clusters[matched]
-                weights = cls_scores[idxs]
-                w_sum = weights.sum()
-                cluster_boxes[matched] = (cls_boxes[idxs] * weights[:, None]).sum(0) / w_sum
-            else:
-                clusters.append([i])
-                cluster_boxes.append(cls_boxes[i].copy())
-
-        for c_idx, idxs in enumerate(clusters):
-            weights = cls_scores[idxs]
-            score = weights.mean()
-            fused_boxes.append(cluster_boxes[c_idx])
-            fused_scores.append(score)
-            fused_labels.append(cls)
+
+    order = all_scores.argsort()[::-1]
+    all_boxes = all_boxes[order]
+    all_scores = all_scores[order]
+
+    clusters: list[list[int]] = []
+    cluster_boxes: list[np.ndarray] = []
+
+    for i in range(len(all_boxes)):
+        matched = -1
+        best_iou = iou_thr
+        for c_idx, c_box in enumerate(cluster_boxes):
+            xx1 = max(all_boxes[i, 0], c_box[0])
+            yy1 = max(all_boxes[i, 1], c_box[1])
+            xx2 = min(all_boxes[i, 2], c_box[2])
+            yy2 = min(all_boxes[i, 3], c_box[3])
+            inter = max(0, xx2 - xx1) * max(0, yy2 - yy1)
+            a1 = (all_boxes[i, 2] - all_boxes[i, 0]) * (all_boxes[i, 3] - all_boxes[i, 1])
+            a2 = (c_box[2] - c_box[0]) * (c_box[3] - c_box[1])
+            iou = inter / (a1 + a2 - inter + 1e-9)
+            if iou > best_iou:
+                best_iou = iou
+                matched = c_idx
+        if matched >= 0:
+            clusters[matched].append(i)
+            idxs = clusters[matched]
+            weights = all_scores[idxs]
+            w_sum = weights.sum()
+            cluster_boxes[matched] = (all_boxes[idxs] * weights[:, None]).sum(0) / w_sum
+        else:
+            clusters.append([i])
+            cluster_boxes.append(all_boxes[i].copy())
+
+    fused_boxes, fused_scores = [], []
+    for c_idx, idxs in enumerate(clusters):
+        weights = all_scores[idxs]
+        fused_boxes.append(cluster_boxes[c_idx])
+        fused_scores.append(weights.mean())
 
     if not fused_boxes:
-        return np.empty((0, 4)), np.empty(0), np.empty(0)
-    return np.array(fused_boxes), np.array(fused_scores), np.array(fused_labels)
+        return np.empty((0, 4)), np.empty(0)
+    return np.array(fused_boxes), np.array(fused_scores)
 
 
 class BoundingBox(BaseModel):
@@ -126,100 +102,100 @@ 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
+        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 = CONF_THRESH
         self.tta_conf_threshold = TTA_CONF_THRESH
         self.iou_threshold = IOU_THRESH
 
     def __repr__(self) -> str:
-        return f"VehicleDetect Miner v7 2-pass TTA + per-class conf"
-
-    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, conf_thresh: float | None = None
-                    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        return f"DetectPerson Miner v6 2-pass TTA + WBF iou={WBF_IOU_THR}"
+
+    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,
+                    conf_thresh: float | None = None) -> tuple[np.ndarray, np.ndarray]:
         pred = raw[0]
+        if pred.ndim != 2:
+            return np.empty((0, 4)), np.empty(0)
         if pred.shape[0] < pred.shape[1]:
-            pred = pred.T
-        bboxes_cx = pred[:, :4]
+            pred = pred.transpose(1, 0)
+        if pred.shape[1] < 5:
+            return np.empty((0, 4)), np.empty(0)
+
+        boxes = pred[:, :4]
         cls_scores = pred[:, 4:]
-        cls_ids = np.argmax(cls_scores, axis=1)
+        if cls_scores.shape[1] == 0:
+            return np.empty((0, 4)), np.empty(0)
+
         confs = np.max(cls_scores, axis=1)
-        thresh = conf_thresh if conf_thresh is not None else CONF_THRESH_DEFAULT
-        mask = confs >= thresh
-        if not mask.any():
-            return np.empty((0, 4)), np.empty(0), np.empty(0, dtype=int)
-        bboxes_cx, confs, cls_ids = bboxes_cx[mask], confs[mask], cls_ids[mask]
-        cx, cy, bw, bh = bboxes_cx[:, 0], bboxes_cx[:, 1], bboxes_cx[:, 2], bboxes_cx[:, 3]
-        x1 = np.clip((cx - bw / 2 - pad_l) / ratio, 0, orig_w)
-        y1 = np.clip((cy - bh / 2 - pad_t) / ratio, 0, orig_h)
-        x2 = np.clip((cx + bw / 2 - pad_l) / ratio, 0, orig_w)
-        y2 = np.clip((cy + bh / 2 - pad_t) / ratio, 0, orig_h)
-        return np.stack([x1, y1, x2, y2], axis=1), confs, cls_ids
+        thresh = conf_thresh if conf_thresh is not None else self.conf_threshold
+        keep = confs >= thresh
+        boxes, confs = boxes[keep], confs[keep]
+        if boxes.shape[0] == 0:
+            return np.empty((0, 4)), np.empty(0)
+
+        sx = orig_w / float(self.input_w)
+        sy = orig_h / float(self.input_h)
+        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)
+        return np.stack([x1, y1, x2, y2], axis=1), confs
 
     def _run_single_pass(self, image_bgr: ndarray, conf_thresh: float | None = None
-                         ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+                         ) -> tuple[np.ndarray, np.ndarray]:
         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, conf_thresh)
+        return self._decode_raw(raw, orig_h, orig_w, conf_thresh)
 
     def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
         orig_h, orig_w = image_bgr.shape[:2]
 
-        all_boxes, all_scores, all_labels = [], [], []
+        all_boxes, all_scores = [], []
 
-        def _collect(boxes, confs, cls_ids):
+        def _collect(boxes, confs):
             if len(boxes) == 0:
                 return
-            out_cls = np.array([MODEL_TO_OUT[int(c)] for c in cls_ids])
             norm = boxes.copy()
             norm[:, [0, 2]] /= orig_w
             norm[:, [1, 3]] /= orig_h
             norm = np.clip(norm, 0, 1)
             all_boxes.append(norm)
             all_scores.append(confs)
-            all_labels.append(out_cls)
 
         # Pass 1: original (low threshold for TTA)
         _collect(*self._run_single_pass(image_bgr, self.tta_conf_threshold))
 
         # Pass 2: horizontal flip
         flipped = cv2.flip(image_bgr, 1)
-        boxes_f, confs_f, cls_f = self._run_single_pass(flipped, self.tta_conf_threshold)
+        boxes_f, confs_f = self._run_single_pass(flipped, self.tta_conf_threshold)
         if len(boxes_f):
             boxes_f[:, 0], boxes_f[:, 2] = orig_w - boxes_f[:, 2], orig_w - boxes_f[:, 0]
-        _collect(boxes_f, confs_f, cls_f)
+        _collect(boxes_f, confs_f)
 
         # (1.2x crop pass REMOVED — adds more FPs than TPs)
 
         if not all_boxes:
             return []
 
-        fused_boxes, fused_scores, fused_labels = _wbf(
-            all_boxes, all_scores, all_labels,
+        fused_boxes, fused_scores = _wbf(
+            all_boxes, all_scores,
             iou_thr=WBF_IOU_THR, skip_box_thr=WBF_SKIP_THR,
         )
         if len(fused_boxes) == 0:
@@ -229,16 +205,10 @@ class Miner:
         fused_boxes[:, [0, 2]] *= orig_w
         fused_boxes[:, [1, 3]] *= orig_h
 
-        # Apply per-class conf threshold after WBF
-        keep_mask = np.array([
-            fused_scores[i] >= CONF_PER_CLASS.get(int(fused_labels[i]), CONF_THRESH_DEFAULT)
-            for i in range(len(fused_scores))
-        ])
-        if not keep_mask.any():
-            return []
-        fused_boxes = fused_boxes[keep_mask]
-        fused_scores = fused_scores[keep_mask]
-        fused_labels = fused_labels[keep_mask]
+        # Apply final conf threshold after WBF
+        keep = fused_scores >= self.conf_threshold
+        fused_boxes = fused_boxes[keep]
+        fused_scores = fused_scores[keep]
 
         out: list[BoundingBox] = []
         for i in range(len(fused_boxes)):
@@ -248,7 +218,7 @@ class Miner:
                 y1=max(0, min(orig_h, math.floor(b[1]))),
                 x2=max(0, min(orig_w, math.ceil(b[2]))),
                 y2=max(0, min(orig_h, math.ceil(b[3]))),
-                cls_id=int(fused_labels[i]),
+                cls_id=0,
                 conf=max(0.0, min(1.0, float(fused_scores[i]))),
             ))
         return out

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