Glas42 commited on
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1 Parent(s): 951bd84

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README.md CHANGED
@@ -1,3 +1,3 @@
1
- ---
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- license: mit
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- ---
 
1
+ This YOLOv5 model was created and trained by Glas42 (2024-02-02)
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+
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+ This model can be used to detect the position of the minimap and the blue arrow on the minimap in Euro Truck Simulator 2 or American Truck Simulator.
model/ExampleUsage.py ADDED
@@ -0,0 +1,96 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ try:
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+ import subprocess
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+ except:
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+ print('"subprocess" module is not available, exiting...')
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+ exit()
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+ try:
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+ import sys
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+ except:
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+ print('"sys" module is not available, exiting...')
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+ exit()
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+ try:
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+ import time
13
+ except:
14
+ print('"time" module is not available, exiting...')
15
+ exit()
16
+ try:
17
+ import os
18
+ except:
19
+ print('"os" module is not available, exiting...')
20
+ exit()
21
+ try:
22
+ import numpy as np
23
+ except:
24
+ subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"])
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+ import numpy as np
26
+ try:
27
+ import pyautogui
28
+ except:
29
+ subprocess.check_call([sys.executable, "-m", "pip", "install", "pyautogui"])
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+ import pyautogui
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+ try:
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+ import dxcam
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+ except:
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+ subprocess.check_call([sys.executable, "-m", "pip", "install", "dxcam"])
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+ import dxcam
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+ try:
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+ import torch
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+ except:
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+ subprocess.check_call([sys.executable, "-m", "pip", "install", "torch"])
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+ import torch
41
+ try:
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+ import cv2
43
+ except:
44
+ subprocess.check_call([sys.executable, "-m", "pip", "install", "opencv-python"])
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+ import cv2
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+ try:
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+ import pathlib
48
+ except:
49
+ subprocess.check_call([sys.executable, "-m", "pip", "install", "pathlib"])
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+ import pathlib
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+
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+ temp = pathlib.PosixPath
53
+ pathlib.PosixPath = pathlib.WindowsPath
54
+
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+ current_file_path = os.path.dirname(os.path.abspath(__file__))
56
+ model_path = os.path.join(current_file_path, 'best.pt')
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+ model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path, force_reload=True)
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+
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+ screen_width, screen_height = pyautogui.size()
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+ camera = dxcam.create(device_idx=0,output_color="RGB")
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+
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+ cv2.namedWindow('YOLOv5 Detection', cv2.WINDOW_NORMAL)
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+ cv2.resizeWindow('YOLOv5 Detection', 960, 540)
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+ cv2.setWindowProperty('YOLOv5 Detection', cv2.WND_PROP_TOPMOST, 1)
65
+
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+ while True:
67
+ start_time = time.time()
68
+ frame = camera.grab(region=(0, 0, screen_width, screen_height))
69
+ if frame is None: continue
70
+ rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
71
+
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+ results = model(rgb_frame)
73
+
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+ boxes = results.pandas().xyxy[0]
75
+
76
+ for _, box in boxes.iterrows():
77
+ label = box['name']
78
+ score = box['confidence']
79
+ x, y, w, h = int(box['xmin']), int(box['ymin']), int(box['xmax'] - box['xmin']), int(box['ymax'] - box['ymin'])
80
+
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+ if label in ['map']:
82
+ cv2.rectangle(rgb_frame, (x, y), (x + w, y + h), (0, 255, 255), 3)
83
+ cv2.putText(rgb_frame, f"{score:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 255), 2, cv2.LINE_AA)
84
+ if label in ['arrow']:
85
+ cv2.rectangle(rgb_frame, (x, y), (x + w, y + h), (255, 0, 0), 3)
86
+ cv2.putText(rgb_frame, f"{score:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 0, 0), 2, cv2.LINE_AA)
87
+
88
+ fps = round(1 / (time.time() - start_time), 1)
89
+ cv2.putText(rgb_frame, f"fps: {fps}", (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
90
+ cv2.imshow('YOLOv5 Detection', rgb_frame)
91
+ cv2.resizeWindow('YOLOv5 Detection', 854, 480)
92
+
93
+ if cv2.waitKey(1) & 0xFF == ord('q'):
94
+ break
95
+
96
+ cv2.destroyAllWindows()
model/HowToUse.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
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+ The model contains 2 classes: "map" and "arrow"
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+ You can run the ExampleUsage.py to test the model.
model/YOLOv5_MapDetectionModel.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:d516ade0ff4092adf3a77e215ceb70e85412cb757b58d37abb033de21d23d50e
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+ size 173038956
other/F1_curve.png ADDED
other/PR_curve.png ADDED
other/P_curve.png ADDED
other/YOLO_YAML.yaml ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
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+ train: ../train_data/images/train/
2
+ val: ../train_data/images/val/
3
+
4
+ nc: 2
5
+
6
+ names:
7
+ 0: map
8
+ 1: arrow
other/best.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:d516ade0ff4092adf3a77e215ceb70e85412cb757b58d37abb033de21d23d50e
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+ size 173038956
other/hyp.yaml ADDED
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+ lr0: 0.01
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+ lrf: 0.01
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+ momentum: 0.937
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+ weight_decay: 0.0005
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+ warmup_epochs: 3.0
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+ warmup_momentum: 0.8
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+ warmup_bias_lr: 0.1
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+ box: 0.05
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+ cls: 0.5
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+ cls_pw: 1.0
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+ obj: 1.0
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+ obj_pw: 1.0
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+ iou_t: 0.2
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+ anchor_t: 4.0
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+ fl_gamma: 0.0
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+ hsv_h: 0.015
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+ hsv_s: 0.7
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+ hsv_v: 0.4
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+ degrees: 0.0
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+ translate: 0.1
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+ scale: 0.5
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+ shear: 0.0
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+ perspective: 0.0
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+ flipud: 0.0
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+ fliplr: 0.5
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+ mosaic: 1.0
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+ mixup: 0.0
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+ copy_paste: 0.0
other/labels.jpg ADDED
other/labels_correlogram.jpg ADDED
other/last.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8ec733d54eff3e77fa9be4807b5ffc5618a26335473891a2330ca04dc06f5094
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+ size 173038956
other/opt.yaml ADDED
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1
+ weights: yolov5x.pt
2
+ cfg: ''
3
+ data: /content/yolov5/data/YOLO_YAML.yaml
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+ hyp:
5
+ lr0: 0.01
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+ lrf: 0.01
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+ momentum: 0.937
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+ weight_decay: 0.0005
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+ warmup_epochs: 3.0
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+ warmup_momentum: 0.8
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+ warmup_bias_lr: 0.1
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+ box: 0.05
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+ cls: 0.5
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+ cls_pw: 1.0
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+ obj: 1.0
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+ obj_pw: 1.0
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+ iou_t: 0.2
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+ anchor_t: 4.0
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+ fl_gamma: 0.0
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+ hsv_h: 0.015
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+ hsv_s: 0.7
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+ hsv_v: 0.4
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+ degrees: 0.0
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+ translate: 0.1
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+ scale: 0.5
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+ shear: 0.0
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+ perspective: 0.0
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+ flipud: 0.0
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+ fliplr: 0.5
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+ mosaic: 1.0
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+ mixup: 0.0
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+ copy_paste: 0.0
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+ epochs: 300
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+ batch_size: 16
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+ imgsz: 640
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+ rect: false
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+ resume: false
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+ nosave: false
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+ noval: false
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+ noautoanchor: false
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+ noplots: false
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+ evolve: null
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+ evolve_population: data/hyps
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+ resume_evolve: null
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+ bucket: ''
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+ cache: ram
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+ image_weights: false
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+ device: ''
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+ multi_scale: false
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+ single_cls: false
51
+ optimizer: SGD
52
+ sync_bn: false
53
+ workers: 8
54
+ project: runs/train
55
+ name: exp
56
+ exist_ok: false
57
+ quad: false
58
+ cos_lr: false
59
+ label_smoothing: 0.0
60
+ patience: 100
61
+ freeze:
62
+ - 0
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+ save_period: -1
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+ seed: 0
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+ local_rank: -1
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+ entity: null
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+ upload_dataset: false
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+ bbox_interval: -1
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+ artifact_alias: latest
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+ ndjson_console: false
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+ ndjson_file: false
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+ save_dir: runs/train/exp
other/results.csv ADDED
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+ epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
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+ 0, 0.10881, 0.029967, 0.025558, 0.0028075, 0.48387, 0.02106, 0.0084079, 0.091747, 0.024892, 0.020721, 0.0919, 0.0009, 0.0009
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+ 1, 0.088238, 0.031365, 0.018664, 0.017803, 0.4871, 0.26527, 0.08619, 0.084189, 0.023593, 0.014896, 0.082894, 0.0018937, 0.0018937
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+ 2, 0.085735, 0.029499, 0.013969, 0.6055, 0.14062, 0.11514, 0.024706, 0.090057, 0.019456, 0.011732, 0.073881, 0.0028809, 0.0028809
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+ 3, 0.083486, 0.02594, 0.010261, 0.70338, 0.28065, 0.21634, 0.05721, 0.08745, 0.016635, 0.0094308, 0.064861, 0.0038614, 0.0038614
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+ 4, 0.085117, 0.022376, 0.0090667, 0.73267, 0.40709, 0.25128, 0.085485, 0.087582, 0.014158, 0.0080086, 0.055835, 0.0048353, 0.0048353
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+ 5, 0.076394, 0.022272, 0.0089515, 0.64049, 0.37419, 0.14393, 0.031483, 0.095493, 0.011267, 0.0080688, 0.046803, 0.0058027, 0.0058027
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+ 6, 0.078919, 0.019552, 0.0080947, 0.94166, 0.43871, 0.51992, 0.14063, 0.091645, 0.01061, 0.0072997, 0.037763, 0.0067634, 0.0067634
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+ 7, 0.074438, 0.019564, 0.0082371, 0.91134, 0.46396, 0.4481, 0.11928, 0.082796, 0.010593, 0.0073741, 0.028718, 0.0077175, 0.0077175
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+ 8, 0.079697, 0.017161, 0.0076069, 0.52837, 0.72272, 0.71829, 0.24752, 0.083475, 0.0096266, 0.0078056, 0.019665, 0.008665, 0.008665
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+ 9, 0.081632, 0.017174, 0.0077755, 0.52685, 0.56173, 0.4705, 0.11815, 0.082844, 0.0097996, 0.0076225, 0.010606, 0.009606, 0.009606
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+ 10, 0.079621, 0.016455, 0.008091, 0.29727, 0.52556, 0.29908, 0.13857, 0.089345, 0.0093477, 0.0080556, 0.00967, 0.00967, 0.00967
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+ 11, 0.071563, 0.016422, 0.0073752, 0.20362, 0.44516, 0.27487, 0.099722, 0.076466, 0.0091173, 0.0080943, 0.00967, 0.00967, 0.00967
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+ 12, 0.066258, 0.014561, 0.0066067, 0.42919, 0.55308, 0.4667, 0.17752, 0.075065, 0.0083225, 0.0074419, 0.009637, 0.009637, 0.009637
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+ 13, 0.064823, 0.014763, 0.0061842, 0.82799, 0.75175, 0.72378, 0.2643, 0.075989, 0.0078575, 0.0063659, 0.009604, 0.009604, 0.009604
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+ 14, 0.06217, 0.013277, 0.0060365, 0.62412, 0.79371, 0.51517, 0.21692, 0.064032, 0.0081509, 0.0072919, 0.009571, 0.009571, 0.009571
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+ 15, 0.056047, 0.012292, 0.0061464, 0.69088, 0.80097, 0.71245, 0.26502, 0.068337, 0.0067986, 0.006225, 0.009538, 0.009538, 0.009538
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+ 16, 0.0569, 0.011788, 0.0056506, 0.51175, 0.51748, 0.58516, 0.21043, 0.06566, 0.0069546, 0.0068921, 0.009505, 0.009505, 0.009505
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+ 17, 0.054695, 0.011166, 0.0056849, 0.75411, 0.8951, 0.82889, 0.40765, 0.054793, 0.006539, 0.0078639, 0.009472, 0.009472, 0.009472
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+ 18, 0.054116, 0.011268, 0.0056562, 0.93895, 0.85664, 0.92995, 0.48159, 0.051342, 0.0066343, 0.0062474, 0.009439, 0.009439, 0.009439
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+ 19, 0.049771, 0.010819, 0.0053345, 0.57847, 0.73427, 0.61605, 0.27429, 0.062996, 0.0069628, 0.0057082, 0.009406, 0.009406, 0.009406
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+ 20, 0.050973, 0.010154, 0.0055708, 0.82442, 0.8007, 0.8567, 0.35313, 0.057285, 0.0066755, 0.0058725, 0.009373, 0.009373, 0.009373
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+ 21, 0.049423, 0.0096976, 0.005468, 0.97552, 0.87413, 0.93689, 0.5361, 0.047531, 0.0063047, 0.0061149, 0.00934, 0.00934, 0.00934
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+ 22, 0.046368, 0.010441, 0.005292, 0.96999, 0.91712, 0.96461, 0.4814, 0.049364, 0.0062872, 0.0060767, 0.009307, 0.009307, 0.009307
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+ 23, 0.045412, 0.0098551, 0.0050373, 0.7677, 0.91259, 0.92793, 0.46391, 0.051892, 0.0060322, 0.0056074, 0.009274, 0.009274, 0.009274
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+ 24, 0.046654, 0.0093711, 0.0048933, 0.66605, 0.84266, 0.81081, 0.46967, 0.048678, 0.0061735, 0.0055004, 0.009241, 0.009241, 0.009241
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+ 25, 0.04611, 0.0082727, 0.0052231, 0.78333, 0.8951, 0.93089, 0.55068, 0.04339, 0.0055563, 0.0056139, 0.009208, 0.009208, 0.009208
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+ 26, 0.042423, 0.0085856, 0.0047693, 0.77988, 0.94689, 0.92265, 0.69392, 0.042126, 0.005401, 0.0055681, 0.009175, 0.009175, 0.009175
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+ 27, 0.042581, 0.0083878, 0.0050013, 0.43892, 0.79371, 0.62164, 0.28755, 0.061079, 0.006282, 0.005443, 0.009142, 0.009142, 0.009142
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+ 28, 0.044564, 0.0088037, 0.0047522, 0.98273, 0.8986, 0.96086, 0.56512, 0.04072, 0.0053404, 0.0056341, 0.009109, 0.009109, 0.009109
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+ 29, 0.042588, 0.0075419, 0.0050051, 0.76832, 0.86364, 0.90286, 0.41188, 0.043829, 0.005411, 0.0053627, 0.009076, 0.009076, 0.009076
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+ 30, 0.043091, 0.0085627, 0.0049721, 0.64502, 0.87784, 0.91758, 0.46834, 0.04942, 0.0055405, 0.0054914, 0.009043, 0.009043, 0.009043
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+ 31, 0.04415, 0.0077877, 0.0046041, 0.96937, 0.93175, 0.96156, 0.55883, 0.049001, 0.0053883, 0.0053125, 0.00901, 0.00901, 0.00901
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+ 32, 0.040302, 0.0083369, 0.0047003, 0.99104, 0.93637, 0.97119, 0.64858, 0.037483, 0.0051792, 0.0054192, 0.008977, 0.008977, 0.008977
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+ 33, 0.03856, 0.0075519, 0.0047275, 0.90303, 0.94545, 0.96094, 0.55588, 0.03765, 0.0052658, 0.0054796, 0.008944, 0.008944, 0.008944
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+ 34, 0.039887, 0.0079571, 0.0048401, 0.95859, 0.98252, 0.98643, 0.6153, 0.036361, 0.0054911, 0.0054834, 0.008911, 0.008911, 0.008911
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+ 36, 0.04064, 0.0082025, 0.0045618, 0.93354, 0.90559, 0.96362, 0.62959, 0.040529, 0.0054932, 0.0053316, 0.008845, 0.008845, 0.008845
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+ 37, 0.036924, 0.0077592, 0.0043956, 0.79898, 0.85015, 0.88777, 0.52089, 0.039489, 0.0051672, 0.0052859, 0.008812, 0.008812, 0.008812
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+ 38, 0.037651, 0.0072606, 0.0044692, 0.78073, 0.97902, 0.91698, 0.69509, 0.04228, 0.0050467, 0.0051583, 0.008779, 0.008779, 0.008779
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+ 39, 0.037157, 0.0074865, 0.0044351, 0.97192, 0.97432, 0.9906, 0.52766, 0.03882, 0.0050249, 0.0051764, 0.008746, 0.008746, 0.008746
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+ 40, 0.036186, 0.0072843, 0.0041793, 0.96846, 0.99368, 0.99219, 0.62659, 0.032476, 0.0047632, 0.0051666, 0.008713, 0.008713, 0.008713
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+ 41, 0.035882, 0.0076687, 0.0047039, 0.97532, 0.99301, 0.99361, 0.68429, 0.032345, 0.0046698, 0.0051785, 0.00868, 0.00868, 0.00868
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+ 42, 0.035412, 0.0075179, 0.004305, 0.98538, 0.9965, 0.99366, 0.66996, 0.031374, 0.0047407, 0.0052768, 0.008647, 0.008647, 0.008647
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+ 43, 0.035171, 0.0072297, 0.0043073, 0.99071, 1, 0.99396, 0.61614, 0.033774, 0.004637, 0.0052014, 0.008614, 0.008614, 0.008614
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+ 49, 0.032322, 0.0062702, 0.0042723, 0.81287, 0.98601, 0.94065, 0.71, 0.033121, 0.0044651, 0.0051219, 0.008416, 0.008416, 0.008416
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204
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215
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229
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230
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236
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239
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240
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241
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242
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243
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244
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250
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251
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252
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253
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254
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255
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256
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257
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258
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259
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260
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262
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263
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272
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