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.gitattributes

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config.yml

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+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install "torch==2.7.1" "torchvision==0.22.1"
+    - pip install "ultralytics==8.3.222" "opencv-python-headless" "numpy" "pydantic"
+    - pip install scikit-learn cryptography
+    - pip install onnxruntime-gpu
+  set_workdir: /app
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 24
+  exclude:
+    - "5090"
+    - b200
+    - h200
+    - mi300x
+
+Chute:
+  timeout_seconds: 900
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 3600

football_object_detection.onnx

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+size 38568385

hrnetv2_w48.yaml

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+MODEL:
+  IMAGE_SIZE: [960, 540]
+  NUM_JOINTS: 58
+  PRETRAIN: ''
+  EXTRA:
+    FINAL_CONV_KERNEL: 1
+    STAGE1:
+      NUM_MODULES: 1
+      NUM_BRANCHES: 1
+      BLOCK: BOTTLENECK
+      NUM_BLOCKS: [4]
+      NUM_CHANNELS: [64]
+      FUSE_METHOD: SUM
+    STAGE2:
+      NUM_MODULES: 1
+      NUM_BRANCHES: 2
+      BLOCK: BASIC
+      NUM_BLOCKS: [4, 4]
+      NUM_CHANNELS: [48, 96]
+      FUSE_METHOD: SUM
+    STAGE3:
+      NUM_MODULES: 4
+      NUM_BRANCHES: 3
+      BLOCK: BASIC
+      NUM_BLOCKS: [4, 4, 4]
+      NUM_CHANNELS: [48, 96, 192]
+      FUSE_METHOD: SUM
+    STAGE4:
+      NUM_MODULES: 3
+      NUM_BRANCHES: 4
+      BLOCK: BASIC
+      NUM_BLOCKS: [4, 4, 4, 4]
+      NUM_CHANNELS: [48, 96, 192, 384]
+      FUSE_METHOD: SUM
+

inference.cpython-312-x86_64-linux-gnu.so

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+size 520088

keypoint

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keypoint_helper.cpython-312-x86_64-linux-gnu.so

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miner.py

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+from pathlib import Path
+from typing import List, Tuple, Dict
+import sys
+import os
+
+from numpy import ndarray
+from pydantic import BaseModel
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from team_cluster import TeamClassifier
+from utils import (
+    BoundingBox, 
+    Constants,
+)
+from inference import predict_batch, load_model
+
+
+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:
+    SMALL_CONTAINED_IOA = Constants.SMALL_CONTAINED_IOA
+    SMALL_RATIO_MAX = Constants.SMALL_RATIO_MAX
+    SINGLE_PLAYER_HUE_PIVOT = Constants.SINGLE_PLAYER_HUE_PIVOT
+    CORNER_INDICES = Constants.CORNER_INDICES
+    KEYPOINTS_CONFIDENCE = Constants.KEYPOINTS_CONFIDENCE
+    CORNER_CONFIDENCE = Constants.CORNER_CONFIDENCE
+    GOALKEEPER_POSITION_MARGIN = Constants.GOALKEEPER_POSITION_MARGIN
+    MIN_SAMPLES_FOR_FIT = 16  # Minimum player crops needed before fitting TeamClassifier
+    MAX_SAMPLES_FOR_FIT = 800  # Maximum samples to avoid overfitting
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        print("model laoding")
+        self.health = load_model(path_hf_repo)
+
+    def __repr__(self) -> str:
+        return self.health
+
+    def predict_batch(self, batch_images: List[ndarray], offset: int, n_keypoints: int) -> List[TVFrameResult]:    
+        results = predict_batch(
+            batch_images,
+            offset,
+            n_keypoints
+        )
+        return results

osnet_model.pth.tar-100

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pitch.cpython-312-x86_64-linux-gnu.so

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