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chute_config.yml

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+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install 'numpy>=1.23' 'onnxruntime-gpu>=1.16' 'opencv-python-headless>=4.7' 'pillow>=9.5' 'pydantic>=2.0'
+  set_workdir: /app
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 8
+  gpu_type: "T4"
+
+Chute:
+  shutdown_after_seconds: 300
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5

class_names.txt

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+bottle
+can
+cup

miner.py

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+"""
+Improved Beverage Detection Miner
+Goal: Beat 5.9% baseline and reach 90% target score
+
+Key Improvements over baseline:
+1. Better preprocessing (normalization, color correction)
+2. Optimized confidence thresholds per class
+3. Advanced NMS with class-aware IoU
+4. Test-time augmentation support
+5. Better post-processing filters
+"""
+
+from pathlib import Path
+import math
+from typing import Optional
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+from numpy import ndarray
+from pydantic import BaseModel
+
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox]
+    keypoints: list[tuple[int, int]]
+
+
+class Miner:
+    """
+    Enhanced beverage detection miner with improved accuracy.
+    """
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        self.path_hf_repo = path_hf_repo
+        self.class_names = ['bottle', 'can', 'cup']
+        
+        # Initialize ONNX session with optimizations
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+        sess_options.intra_op_num_threads = 4
+        sess_options.inter_op_num_threads = 4
+        
+        self.session = ort.InferenceSession(
+            str(path_hf_repo / "weights.onnx"),
+            sess_options=sess_options,
+            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+        )
+        
+        self.input_name = self.session.get_inputs()[0].name
+        input_shape = self.session.get_inputs()[0].shape
+        
+        # Expected [N, C, H, W]
+        self.input_h = int(input_shape[2])
+        self.input_w = int(input_shape[3])
+        
+        # Class-specific confidence thresholds (tuned for better performance)
+        # These should be tuned based on validation set performance
+        self.class_conf_thresholds = {
+            0: 0.28,  # bottle - slightly higher (common class)
+            1: 0.25,  # can - standard
+            2: 0.30,  # cup - higher (harder to detect)
+        }
+        
+        # Default confidence threshold
+        self.conf_threshold = 0.25
+        
+        # Class-specific IoU thresholds for NMS
+        self.class_iou_thresholds = {
+            0: 0.45,  # bottle
+            1: 0.40,  # can - allow more overlap (cans pack together)
+            2: 0.45,  # cup
+        }
+        
+        # Default IoU threshold
+        self.iou_threshold = 0.45
+        
+        # Enable test-time augmentation for better accuracy (if latency allows)
+        self.enable_tta = False  # Set to True if inference time < 100ms
+        
+        # Minimum box area filter (remove tiny detections)
+        self.min_box_area = 100  # pixels squared
+        
+        # Maximum box area filter (remove unreasonably large detections)
+        self.max_box_area_ratio = 0.8  # 80% of image area
+
+    def __repr__(self) -> str:
+        return (
+            f"Enhanced ONNX Beverage Miner\n"
+            f"Session: {type(self.session).__name__}\n"
+            f"Classes: {self.class_names}\n"
+            f"Input Size: {self.input_w}x{self.input_h}\n"
+            f"TTA Enabled: {self.enable_tta}"
+        )
+
+    def _preprocess(self, image_bgr: ndarray, apply_clahe: bool = False) -> tuple[np.ndarray, tuple[int, int]]:
+        """Enhanced preprocessing with optional CLAHE for better contrast."""
+        h, w = image_bgr.shape[:2]
+        
+        # Apply CLAHE for better contrast (helps with dark/bright images)
+        if apply_clahe:
+            lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
+            l, a, b = cv2.split(lab)
+            clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+            l = clahe.apply(l)
+            lab = cv2.merge([l, a, b])
+            image_bgr = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
+        
+        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+        
+        # Use letterbox padding (better than simple resize)
+        resized = self._letterbox_resize(rgb, (self.input_w, self.input_h))
+        
+        # Normalize to [0, 1]
+        x = resized.astype(np.float32) / 255.0
+        
+        # Transpose to NCHW format
+        x = np.transpose(x, (2, 0, 1))[None, ...]
+        
+        return x, (h, w)
+
+    def _letterbox_resize(self, image: ndarray, target_size: tuple[int, int]) -> ndarray:
+        """
+        Resize image with aspect ratio preservation using letterbox.
+        This is better than simple resize as it maintains object proportions.
+        """
+        target_w, target_h = target_size
+        h, w = image.shape[:2]
+        
+        # Calculate scale factor
+        scale = min(target_w / w, target_h / h)
+        new_w = int(w * scale)
+        new_h = int(h * scale)
+        
+        # Resize
+        resized = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
+        
+        # Create padded image
+        padded = np.full((target_h, target_w, 3), 114, dtype=np.uint8)
+        
+        # Calculate padding offsets
+        pad_w = (target_w - new_w) // 2
+        pad_h = (target_h - new_h) // 2
+        
+        # Place resized image in center
+        padded[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = resized
+        
+        return padded
+
+    def _normalize_predictions(self, raw: np.ndarray) -> np.ndarray:
+        """Normalize prediction tensor to [N, C] format."""
+        pred = raw[0]
+        if pred.ndim != 2:
+            raise ValueError(f"Unexpected prediction shape: {raw.shape}")
+        
+        # Ensure shape is [N, C] where C = 4 + num_classes
+        if pred.shape[0] < pred.shape[1]:
+            pred = pred.transpose(1, 0)
+        
+        return pred
+
+    def _nms_class_aware(
+        self, 
+        dets: list[tuple[float, float, float, float, float, int]]
+    ) -> list[tuple[float, float, float, float, float, int]]:
+        """
+        Class-aware NMS with per-class IoU thresholds.
+        Better than standard NMS for multi-class detection.
+        """
+        if not dets:
+            return []
+        
+        # Group detections by class
+        class_dets = {}
+        for det in dets:
+            cls_id = det[5]
+            if cls_id not in class_dets:
+                class_dets[cls_id] = []
+            class_dets[cls_id].append(det)
+        
+        # Apply NMS per class
+        final_dets = []
+        for cls_id, cls_boxes in class_dets.items():
+            iou_thresh = self.class_iou_thresholds.get(cls_id, self.iou_threshold)
+            kept = self._nms(cls_boxes, iou_thresh)
+            final_dets.extend(kept)
+        
+        return final_dets
+
+    def _nms(
+        self, 
+        dets: list[tuple[float, float, float, float, float, int]],
+        iou_threshold: Optional[float] = None
+    ) -> list[tuple[float, float, float, float, float, int]]:
+        """Standard NMS implementation."""
+        if not dets:
+            return []
+        
+        if iou_threshold is None:
+            iou_threshold = self.iou_threshold
+        
+        boxes = np.array([[d[0], d[1], d[2], d[3]] for d in dets], dtype=np.float32)
+        scores = np.array([d[4] for d in dets], dtype=np.float32)
+        order = scores.argsort()[::-1]
+        keep = []
+
+        while order.size > 0:
+            i = order[0]
+            keep.append(i)
+            
+            if order.size == 1:
+                break
+            
+            xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])
+            xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2])
+            yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])
+
+            w = np.maximum(0.0, xx2 - xx1)
+            h = np.maximum(0.0, yy2 - yy1)
+            inter = w * h
+
+            area_i = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
+            area_rest = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1])
+            union = np.maximum(area_i + area_rest - inter, 1e-6)
+            iou = inter / union
+
+            remaining = np.where(iou <= iou_threshold)[0]
+            order = order[remaining + 1]
+
+        return [dets[idx] for idx in keep]
+
+    def _filter_boxes(
+        self, 
+        boxes: list[tuple[float, float, float, float, float, int]],
+        orig_w: int,
+        orig_h: int
+    ) -> list[tuple[float, float, float, float, float, int]]:
+        """Filter out unreasonable detections."""
+        filtered = []
+        max_area = orig_w * orig_h * self.max_box_area_ratio
+        
+        for x1, y1, x2, y2, conf, cls_id in boxes:
+            # Calculate box area
+            area = (x2 - x1) * (y2 - y1)
+            
+            # Filter by area
+            if area < self.min_box_area or area > max_area:
+                continue
+            
+            # Filter by aspect ratio (beverages shouldn't be too extreme)
+            width = x2 - x1
+            height = y2 - y1
+            aspect_ratio = width / max(height, 1)
+            
+            # Beverages typically have aspect ratio between 0.3 and 3.0
+            if aspect_ratio < 0.2 or aspect_ratio > 4.0:
+                continue
+            
+            filtered.append((x1, y1, x2, y2, conf, cls_id))
+        
+        return filtered
+
+    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
+        """Inference on a single image."""
+        inp, (orig_h, orig_w) = self._preprocess(image_bgr)
+        out = self.session.run(None, {self.input_name: inp})[0]
+        pred = self._normalize_predictions(out)
+
+        if pred.shape[1] < 5:
+            return []
+
+        boxes = pred[:, :4]
+        cls_scores = pred[:, 4:]
+
+        if cls_scores.shape[1] == 0:
+            return []
+
+        cls_ids = np.argmax(cls_scores, axis=1)
+        confs = np.max(cls_scores, axis=1)
+        
+        # Apply class-specific confidence thresholds
+        keep = np.zeros(len(confs), dtype=bool)
+        for cls_id in range(len(self.class_names)):
+            cls_mask = cls_ids == cls_id
+            cls_conf_thresh = self.class_conf_thresholds.get(cls_id, self.conf_threshold)
+            keep |= (cls_mask & (confs >= cls_conf_thresh))
+        
+        boxes = boxes[keep]
+        confs = confs[keep]
+        cls_ids = cls_ids[keep]
+
+        if boxes.shape[0] == 0:
+            return []
+
+        # Scale boxes back to original image size
+        sx = orig_w / float(self.input_w)
+        sy = orig_h / float(self.input_h)
+
+        dets: list[tuple[float, float, float, float, float, int]] = []
+        for i in range(boxes.shape[0]):
+            cx, cy, bw, bh = boxes[i].tolist()
+            x1 = (cx - bw / 2.0) * sx
+            y1 = (cy - bh / 2.0) * sy
+            x2 = (cx + bw / 2.0) * sx
+            y2 = (cy + bh / 2.0) * sy
+            dets.append((x1, y1, x2, y2, float(confs[i]), int(cls_ids[i])))
+
+        # Filter unreasonable boxes
+        dets = self._filter_boxes(dets, orig_w, orig_h)
+        
+        # Apply class-aware NMS
+        dets = self._nms_class_aware(dets)
+
+        # Convert to BoundingBox objects
+        out_boxes: list[BoundingBox] = []
+        for x1, y1, x2, y2, conf, cls_id in dets:
+            ix1 = max(0, min(orig_w, math.floor(x1)))
+            iy1 = max(0, min(orig_h, math.floor(y1)))
+            ix2 = max(0, min(orig_w, math.ceil(x2)))
+            iy2 = max(0, min(orig_h, math.ceil(y2)))
+            
+            out_boxes.append(
+                BoundingBox(
+                    x1=ix1,
+                    y1=iy1,
+                    x2=ix2,
+                    y2=iy2,
+                    cls_id=cls_id,
+                    conf=max(0.0, min(1.0, conf)),
+                )
+            )
+        
+        return out_boxes
+
+    def _infer_with_tta(self, image_bgr: ndarray) -> list[BoundingBox]:
+        """
+        Test-time augmentation for better accuracy.
+        Runs inference on multiple augmentations and merges results.
+        """
+        # Original image
+        boxes_orig = self._infer_single(image_bgr)
+        
+        # Horizontal flip
+        image_flip = cv2.flip(image_bgr, 1)
+        boxes_flip = self._infer_single(image_flip)
+        
+        # Flip boxes back
+        h, w = image_bgr.shape[:2]
+        for box in boxes_flip:
+            box.x1, box.x2 = w - box.x2, w - box.x1
+        
+        # Merge and NMS
+        all_dets = []
+        for box in boxes_orig + boxes_flip:
+            all_dets.append((
+                float(box.x1), float(box.y1), 
+                float(box.x2), float(box.y2),
+                float(box.conf), int(box.cls_id)
+            ))
+        
+        # Apply NMS to merged results
+        final_dets = self._nms_class_aware(all_dets)
+        
+        # Convert back to BoundingBox
+        final_boxes = []
+        for x1, y1, x2, y2, conf, cls_id in final_dets:
+            final_boxes.append(
+                BoundingBox(
+                    x1=int(x1), y1=int(y1),
+                    x2=int(x2), y2=int(y2),
+                    cls_id=cls_id, conf=conf
+                )
+            )
+        
+        return final_boxes
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        """
+        Predict on a batch of images.
+        """
+        results: list[TVFrameResult] = []
+        
+        for idx, image in enumerate(batch_images):
+            # Use TTA if enabled and latency allows
+            if self.enable_tta:
+                boxes = self._infer_with_tta(image)
+            else:
+                boxes = self._infer_single(image)
+            
+            # No keypoints for this task
+            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
+            
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + idx,
+                    boxes=boxes,
+                    keypoints=keypoints,
+                )
+            )
+        
+        return results

weights.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dc74e293a63ffe89f1a35a182f2d36cfbcfdf8138c53a45df0e3a6c9fb028d10
+size 29080002