scorevision: push artifact

miner.py

@@ -1,12 +1,37 @@
-# Build: 2026-05-29 23:30 UTC R11 redeploy (force new revision)
+"""miner.py — uploaded to artur7236/turbovision-beverage HF repo.
+
+Round 16 (R16): YOLO11s fine-tuned on dataset_v11 = batches 1+2 manually-reviewed
++ batch 3 raw (587 SAM3-anchored images, validator-distribution).
+
+Training (on RTX PRO 6000 Blackwell, 150 epochs, AdamW auto-lr, batch=32):
+  - val mAP50 = 0.878 (cup 0.890 / bottle 0.855 / can 0.890)
+  - val mAP50-95 = 0.644
+  - precision 0.886 (cup 0.856 / bottle 0.915 / can 0.887)
+  - recall 0.786
+  - close_mosaic=15 final epochs for sharper boxes
+  - patience=30 (didn't trigger; ran full 150)
+
+Inference (kept identical to R5/R6 to preserve calibration):
+  - imgsz=1280, conf=0.50, iou=0.45, augment=True (hflip TTA)
+  - cross-class NMS at IoU 0.6 (validator counts cross-class overlaps as FP)
+
+Required by the chute template:
+  - class Miner with __init__(self, path_hf_repo: Path)
+  - predict_batch(batch_images, offset, n_keypoints) -> list[TVFrameResult]
+  - BoundingBox + TVFrameResult pydantic models defined in this file
+  - No imports from anywhere except stdlib + site-packages
+"""
 from pathlib import Path
-import math
 
-import cv2
 import numpy as np
-import onnxruntime as ort
 from numpy import ndarray
 from pydantic import BaseModel
+from ultralytics import YOLO
+
+
+# Class index follows the manifest objects order for Detect-beverage-detect:
+# [cup, bottle, can] → cls_id 0, 1, 2.
+CLASS_NAMES = ["cup", "bottle", "can"]
 
 
 class BoundingBox(BaseModel):
@@ -24,478 +49,98 @@ class TVFrameResult(BaseModel):
     keypoints: list[tuple[int, int]]
 
 
-class Miner:
-    """ONNX Runtime miner. Hard global NMS + sanity filter + dedup + flip TTA, with per-class rescue bonus."""
-
-    class_names = ["cup", "bottle", "can"]
-    input_size = 1280
-    iou_thres = 0.4
-    cross_iou_thresh = 0.7
-    min_side = 8.0
-    min_box_area = 100.0
-    max_aspect_ratio = 10.0
-    max_det = 300
-    _conf_thres_array = np.array([0.6, 0.45, 0.5], dtype=np.float32)
-    _bonus_array = np.array([0.0, 0.0, 0.2], dtype=np.float32)
-
-    def __init__(self, path_hf_repo: Path) -> None:
-        model_path = path_hf_repo / "weights.onnx"
-        print("ORT version:", ort.__version__)
-
-        try:
-            ort.preload_dlls()
-            print("preload_dlls success")
-        except Exception as e:
-            print(f"preload_dlls failed: {e}")
-
-        print("ORT available providers BEFORE session:", ort.get_available_providers())
-
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-
-        try:
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
-            )
-            print("Created ORT session with preferred CUDA provider list")
-        except Exception as e:
-            print(f"CUDA session creation failed, falling back to CPU: {e}")
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CPUExecutionProvider"],
-            )
-
-        print("ORT session providers:", self.session.get_providers())
+def _iou(a: BoundingBox, b: BoundingBox) -> float:
+    x1 = max(a.x1, b.x1)
+    y1 = max(a.y1, b.y1)
+    x2 = min(a.x2, b.x2)
+    y2 = min(a.y2, b.y2)
+    if x2 <= x1 or y2 <= y1:
+        return 0.0
+    inter = (x2 - x1) * (y2 - y1)
+    area_a = max(0, a.x2 - a.x1) * max(0, a.y2 - a.y1)
+    area_b = max(0, b.x2 - b.x1) * max(0, b.y2 - b.y1)
+    union = area_a + area_b - inter
+    return inter / union if union > 0 else 0.0
 
-        for inp in self.session.get_inputs():
-            print("INPUT:", inp.name, inp.shape, inp.type)
-        for out in self.session.get_outputs():
-            print("OUTPUT:", out.name, out.shape, out.type)
 
-        self.input_name = self.session.get_inputs()[0].name
-        self.output_names = [output.name for output in self.session.get_outputs()]
-        self.input_shape = self.session.get_inputs()[0].shape
+def _cross_class_nms(boxes: list[BoundingBox], iou_thresh: float = 0.6) -> list[BoundingBox]:
+    """Suppress cross-class overlapping boxes; keep highest-conf when IoU≥thresh.
 
-        self.input_height = self._safe_dim(self.input_shape[2], default=self.input_size)
-        self.input_width = self._safe_dim(self.input_shape[3], default=self.input_size)
-
-        print(f"ONNX model loaded from: {model_path}")
-        print(f"ONNX providers: {self.session.get_providers()}")
-        print(f"ONNX input: name={self.input_name}, shape={self.input_shape}")
-
-    def __repr__(self) -> str:
-        return (
-            f"ONNXRuntime(session={type(self.session).__name__}, "
-            f"providers={self.session.get_providers()})"
-        )
-
-    @staticmethod
-    def _safe_dim(value, default: int) -> int:
-        return value if isinstance(value, int) and value > 0 else default
-
-    def _letterbox(self, image: ndarray, new_shape: tuple[int, int],
-                   color=(114, 114, 114)
-                   ) -> tuple[ndarray, float, tuple[float, float]]:
-        h, w = image.shape[:2]
-        new_w, new_h = new_shape
-        ratio = min(new_w / w, new_h / h)
-        resized_w = int(round(w * ratio))
-        resized_h = int(round(h * ratio))
-        if (resized_w, resized_h) != (w, h):
-            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
-            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
-        dw = (new_w - resized_w) / 2.0
-        dh = (new_h - resized_h) / 2.0
-        left = int(round(dw - 0.1))
-        right = int(round(dw + 0.1))
-        top = int(round(dh - 0.1))
-        bottom = int(round(dh + 0.1))
-        padded = cv2.copyMakeBorder(image, top, bottom, left, right,
-                                    borderType=cv2.BORDER_CONSTANT, value=color)
-        return padded, ratio, (dw, dh)
-
-    def _preprocess(self, image: ndarray
-                    ) -> tuple[np.ndarray, float, tuple[float, float],
-                               tuple[int, int]]:
-        orig_h, orig_w = image.shape[:2]
-        img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = img.astype(np.float32) / 255.0
-        img = np.transpose(img, (2, 0, 1))[None, ...]
-        img = np.ascontiguousarray(img, dtype=np.float32)
-        return img, ratio, pad, (orig_w, orig_h)
-
-    @staticmethod
-    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
-        w, h = image_size
-        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
-        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
-        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
-        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
+    Ultralytics' default NMS only dedupes WITHIN a class. SN44 counts cross-class
+    overlap as a false positive (only one class can be right per object).
+    """
+    if len(boxes) <= 1:
         return boxes
-
-    @staticmethod
-    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
-        out = np.empty_like(boxes)
-        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-        return out
-
-    @staticmethod
-    def _hard_nms(boxes: np.ndarray, scores: np.ndarray,
-                  iou_thresh: float) -> np.ndarray:
-        n = len(boxes)
-        if n == 0:
-            return np.array([], dtype=np.intp)
-        order = np.argsort(-scores)
-        keep: list[int] = []
-        while len(order) > 0:
-            i = int(order[0])
-            keep.append(i)
-            if len(order) == 1:
+    sorted_boxes = sorted(boxes, key=lambda b: -b.conf)
+    kept: list[BoundingBox] = []
+    for b in sorted_boxes:
+        suppressed = False
+        for k in kept:
+            if _iou(b, k) >= iou_thresh:
+                suppressed = True
                 break
-            rest = order[1:]
-            xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
-            yy1 = np.maximum(boxes[i, 1], boxes[rest, 1])
-            xx2 = np.minimum(boxes[i, 2], boxes[rest, 2])
-            yy2 = np.minimum(boxes[i, 3], boxes[rest, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = (max(0.0, boxes[i, 2] - boxes[i, 0]) *
-                   max(0.0, boxes[i, 3] - boxes[i, 1]))
-            a_r = (np.maximum(0.0, boxes[rest, 2] - boxes[rest, 0]) *
-                   np.maximum(0.0, boxes[rest, 3] - boxes[rest, 1]))
-            iou = inter / (a_i + a_r - inter + 1e-7)
-            order = rest[iou <= iou_thresh]
-        return np.array(keep, dtype=np.intp)
-
-    def _per_class_hard_nms(self, boxes: np.ndarray, scores: np.ndarray,
-                            cls_ids: np.ndarray, iou_thresh: float
-                            ) -> np.ndarray:
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp)
-        all_keep: list[int] = []
-        for c in np.unique(cls_ids):
-            mask = cls_ids == c
-            indices = np.where(mask)[0]
-            keep = self._hard_nms(boxes[mask], scores[mask], iou_thresh)
-            all_keep.extend(indices[keep].tolist())
-        all_keep.sort()
-        return np.array(all_keep, dtype=np.intp)
-
-    def _cross_class_dedup_op(self, boxes: np.ndarray, scores: np.ndarray,
-                              cls_ids: np.ndarray, iou_thresh: float
-                              ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        n = len(boxes)
-        if n <= 1:
-            return boxes, scores, cls_ids
-        boxes = np.asarray(boxes, dtype=np.float32)
-        scores = np.asarray(scores, dtype=np.float32)
-        cls_ids = np.asarray(cls_ids, dtype=np.int32)
-        areas = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) *
-                 np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
-        margins = scores - self._conf_thres_array[cls_ids]
-        order = np.lexsort((-areas, -margins))
-        suppressed = np.zeros(n, dtype=bool)
-        keep: list[int] = []
-        for i in order:
-            if suppressed[i]:
-                continue
-            keep.append(int(i))
-            bi = boxes[i]
-            xx1 = np.maximum(bi[0], boxes[:, 0])
-            yy1 = np.maximum(bi[1], boxes[:, 1])
-            xx2 = np.minimum(bi[2], boxes[:, 2])
-            yy2 = np.minimum(bi[3], boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + areas - inter + 1e-7)
-            dup = iou > iou_thresh
-            dup[i] = False
-            suppressed |= dup
-        keep_idx = np.array(keep, dtype=np.intp)
-        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
-
-    def _filter_sane_boxes(self, boxes: np.ndarray, scores: np.ndarray,
-                           cls_ids: np.ndarray, orig_size: tuple[int, int]
-                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        orig_w, orig_h = orig_size
-        image_area = float(orig_w * orig_h)
-        bw = np.maximum(0.0, boxes[:, 2] - boxes[:, 0])
-        bh = np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
-        area = bw * bh
-        ar = np.where(
-            (bw > 0) & (bh > 0),
-            np.maximum(bw / np.maximum(bh, 1e-6), bh / np.maximum(bw, 1e-6)),
-            np.inf,
-        )
-        keep = (
-            (bw >= self.min_side) & (bh >= self.min_side) &
-            (area >= self.min_box_area) &
-            (area <= 0.95 * image_area) &
-            (ar <= self.max_aspect_ratio)
-        )
-        return boxes[keep], scores[keep], cls_ids[keep]
+        if not suppressed:
+            kept.append(b)
+    return kept
 
-    def _max_score_per_cluster(self, post_boxes: np.ndarray,
-                               post_cls: np.ndarray,
-                               full_boxes: np.ndarray,
-                               full_scores: np.ndarray,
-                               full_cls: np.ndarray,
-                               iou_thresh: float) -> np.ndarray:
-        n = len(post_boxes)
-        if n == 0:
-            return np.empty(0, dtype=np.float32)
-        full_areas = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
-                      np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
-        out = np.empty(n, dtype=np.float32)
-        for i in range(n):
-            bi = post_boxes[i]
-            xx1 = np.maximum(bi[0], full_boxes[:, 0])
-            yy1 = np.maximum(bi[1], full_boxes[:, 1])
-            xx2 = np.minimum(bi[2], full_boxes[:, 2])
-            yy2 = np.minimum(bi[3], full_boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(0.0, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + full_areas - inter + 1e-7)
-            cluster = (iou >= iou_thresh) & (full_cls == post_cls[i])
-            out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0
-        return out
-
-    def _conf_filter_mask(self, scores: np.ndarray,
-                          cls_ids: np.ndarray) -> np.ndarray:
-        """Boolean keep-mask: score >= per-class threshold, with a per-class
-        rescue — if a class has zero boxes passing, admit its top-1 candidate
-        when its score >= (per-class threshold - per-class bonus)."""
-        if len(scores) == 0:
-            return np.zeros(0, dtype=bool)
-        thr = self._conf_thres_array[cls_ids]
-        keep = scores >= thr
-        for c in np.unique(cls_ids):
-            b = float(self._bonus_array[c])
-            if b <= 0.0:
-                continue
-            cm = cls_ids == c
-            if keep[cm].any():
-                continue
-            idx = np.where(cm)[0]
-            top = int(idx[int(np.argmax(scores[idx]))])
-            if scores[top] >= self._conf_thres_array[c] - b:
-                keep[top] = True
-        return keep
-
-    def _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray,
-                           cls_ids: np.ndarray, orig_size: tuple[int, int]
-                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            boxes, scores, cls_ids, orig_size
-        )
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        if len(boxes) > 1:
-            keep = self._hard_nms(boxes, scores, self.iou_thres)
-            boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
-        if len(scores) > self.max_det:
-            top = np.argsort(-scores)[: self.max_det]
-            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
-        if len(boxes) > 1:
-            boxes, scores, cls_ids = self._cross_class_dedup_op(
-                boxes, scores, cls_ids, self.cross_iou_thresh
-            )
-        return boxes, scores, cls_ids
-
-    def _decode_final_dets(self, preds: np.ndarray, ratio: float,
-                           pad: tuple[float, float],
-                           orig_size: tuple[int, int]) -> list[BoundingBox]:
-        if preds.ndim == 3 and preds.shape[0] == 1:
-            preds = preds[0]
-        if preds.ndim != 2 or preds.shape[1] < 6:
-            raise ValueError(f"Unexpected ONNX final-det output shape: {preds.shape}")
-
-        boxes = preds[:, :4].astype(np.float32)
-        scores = preds[:, 4].astype(np.float32)
-        cls_ids = preds[:, 5].astype(np.int32)
-
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes = boxes[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-        if len(boxes) == 0:
-            return []
-
-        pad_w, pad_h = pad
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
-
-        boxes, scores, cls_ids = self._per_view_pipeline(
-            boxes, scores, cls_ids, orig_size
-        )
-        return self._build_results(boxes, scores, cls_ids)
-
-    def _decode_raw_yolo(self, preds: np.ndarray, ratio: float,
-                         pad: tuple[float, float],
-                         orig_size: tuple[int, int]) -> list[BoundingBox]:
-        if preds.ndim != 3 or preds.shape[0] != 1:
-            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
-        preds = preds[0]
-        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
-            preds = preds.T
-        if preds.ndim != 2 or preds.shape[1] < 5:
-            raise ValueError(f"Unexpected raw output shape: {preds.shape}")
-
-        boxes_xywh = preds[:, :4].astype(np.float32)
-        cls_part = preds[:, 4:].astype(np.float32)
-        if cls_part.shape[1] == 1:
-            scores = cls_part[:, 0]
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        else:
-            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
-            scores = cls_part[np.arange(len(cls_part)), cls_ids]
-
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes_xywh = boxes_xywh[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-        if len(boxes_xywh) == 0:
-            return []
-        boxes = self._xywh_to_xyxy(boxes_xywh)
-
-        pad_w, pad_h = pad
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
 
-        boxes, scores, cls_ids = self._per_view_pipeline(
-            boxes, scores, cls_ids, orig_size
-        )
-        return self._build_results(boxes, scores, cls_ids)
-
-    @staticmethod
-    def _build_results(boxes: np.ndarray, scores: np.ndarray,
-                       cls_ids: np.ndarray) -> list[BoundingBox]:
-        results: list[BoundingBox] = []
-        for box, conf, cls_id in zip(boxes, scores, cls_ids):
-            x1, y1, x2, y2 = box.tolist()
-            if x2 <= x1 or y2 <= y1:
-                continue
-            results.append(
-                BoundingBox(
-                    x1=int(math.floor(x1)),
-                    y1=int(math.floor(y1)),
-                    x2=int(math.ceil(x2)),
-                    y2=int(math.ceil(y2)),
-                    cls_id=int(cls_id),
-                    conf=float(conf),
-                )
-            )
-        return results
-
-    def _postprocess(self, output: np.ndarray, ratio: float,
-                     pad: tuple[float, float],
-                     orig_size: tuple[int, int]) -> list[BoundingBox]:
-        if output.ndim == 2 and output.shape[1] >= 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-        return self._decode_raw_yolo(output, ratio, pad, orig_size)
-
-    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
-        if image is None:
-            raise ValueError("Input image is None")
-        if not isinstance(image, np.ndarray):
-            raise TypeError(f"Input is not numpy array: {type(image)}")
-        if image.ndim != 3:
-            raise ValueError(f"Expected HWC image, got shape={image.shape}")
-        if image.shape[2] != 3:
-            raise ValueError(f"Expected 3 channels, got shape={image.shape}")
-        if image.dtype != np.uint8:
-            image = image.astype(np.uint8)
-
-        input_tensor, ratio, pad, orig_size = self._preprocess(image)
-        expected = (1, 3, self.input_height, self.input_width)
-        if input_tensor.shape != expected:
-            raise ValueError(
-                f"Bad input tensor shape={input_tensor.shape}, expected={expected}"
-            )
-
-        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
-        return self._postprocess(outputs[0], ratio, pad, orig_size)
-
-    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
-        boxes_orig = self._predict_single(image)
-        flipped = cv2.flip(image, 1)
-        boxes_flip = self._predict_single(flipped)
-        w = image.shape[1]
-        boxes_flip = [
-            BoundingBox(
-                x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
-                cls_id=b.cls_id, conf=b.conf,
-            )
-            for b in boxes_flip
-        ]
-        all_boxes = boxes_orig + boxes_flip
-        if not all_boxes:
-            return []
+class Miner:
+    IMAGE_SIZE = 1280
+    CONF_THRESH = 0.50
+    IOU_THRESH = 0.45
+    USE_TTA = True
+    CROSS_CLASS_IOU = 0.6
 
-        coords = np.array(
-            [[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32
-        )
-        scores = np.array([b.conf for b in all_boxes], dtype=np.float32)
-        cls_ids = np.array([b.cls_id for b in all_boxes], dtype=np.int32)
+    def __init__(self, path_hf_repo: Path) -> None:
+        weights_path = path_hf_repo / "best.pt"
+        if not weights_path.exists():
+            raise FileNotFoundError(f"missing weights at {weights_path}")
+        self.model = YOLO(str(weights_path))
+        dummy = np.zeros((640, 640, 3), dtype=np.uint8)
+        _ = self.model.predict(dummy, imgsz=self.IMAGE_SIZE, conf=self.CONF_THRESH,
+                               iou=self.IOU_THRESH, augment=self.USE_TTA, verbose=False)
+        print(f"✅ YOLO11s R16 loaded from {weights_path}")
 
-        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
-        if len(hard_keep) == 0:
-            return []
-        if len(hard_keep) > self.max_det:
-            top = np.argsort(-scores[hard_keep])[: self.max_det]
-            hard_keep = hard_keep[top]
-        boosted = self._max_score_per_cluster(
-            coords[hard_keep], cls_ids[hard_keep],
-            coords, scores, cls_ids, self.iou_thres,
+    def __repr__(self) -> str:
+        return (f"YOLO11s_R16(imgsz={self.IMAGE_SIZE}, "
+                f"conf={self.CONF_THRESH}, iou={self.IOU_THRESH}, "
+                f"tta={self.USE_TTA})")
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        results = self.model.predict(
+            batch_images,
+            imgsz=self.IMAGE_SIZE,
+            conf=self.CONF_THRESH,
+            iou=self.IOU_THRESH,
+            augment=self.USE_TTA,
+            verbose=False,
         )
 
-        kept_coords = coords[hard_keep]
-        kept_cls = cls_ids[hard_keep]
-        if len(kept_coords) > 1:
-            kept_coords, boosted, kept_cls = self._cross_class_dedup_op(
-                kept_coords, boosted, kept_cls, self.cross_iou_thresh
-            )
-
-        return [
-            BoundingBox(
-                x1=int(math.floor(kept_coords[j, 0])),
-                y1=int(math.floor(kept_coords[j, 1])),
-                x2=int(math.ceil(kept_coords[j, 2])),
-                y2=int(math.ceil(kept_coords[j, 3])),
-                cls_id=int(kept_cls[j]),
-                conf=float(boosted[j]),
-            )
-            for j in range(len(kept_coords))
-        ]
+        out: list[TVFrameResult] = []
+        kp_zeros = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
+
+        for i, r in enumerate(results):
+            frame_id = offset + i
+            boxes: list[BoundingBox] = []
+            if r.boxes is not None and r.boxes.data is not None:
+                for box in r.boxes.data.cpu().numpy():
+                    x1, y1, x2, y2, conf, cls_id = box.tolist()
+                    cls_id_int = int(cls_id)
+                    if cls_id_int < 0 or cls_id_int >= len(CLASS_NAMES):
+                        continue
+                    xi1, yi1, xi2, yi2 = int(x1), int(y1), int(x2), int(y2)
+                    if xi2 <= xi1 or yi2 <= yi1:
+                        continue
+                    boxes.append(BoundingBox(
+                        x1=xi1, y1=yi1, x2=xi2, y2=yi2,
+                        cls_id=cls_id_int, conf=float(conf),
+                    ))
+            boxes = _cross_class_nms(boxes, iou_thresh=self.CROSS_CLASS_IOU)
+            out.append(TVFrameResult(frame_id=frame_id, boxes=boxes, keypoints=kp_zeros))
 
-    def predict_batch(self, batch_images: list[ndarray], offset: int,
-                      n_keypoints: int) -> list[TVFrameResult]:
-        results: list[TVFrameResult] = []
-        for frame_number_in_batch, image in enumerate(batch_images):
-            try:
-                boxes = self._predict_tta(image)
-            except Exception as e:
-                print(f"Inference failed for frame {offset + frame_number_in_batch}: {e}")
-                boxes = []
-            results.append(
-                TVFrameResult(
-                    frame_id=offset + frame_number_in_batch,
-                    boxes=boxes,
-                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
-                )
-            )
-        return results
+        return out