initial commit

.gitattributes

@@ -33,3 +33,63 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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metrics/plots.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import os
+import cv2
+from pathlib import Path
+from ultralytics import YOLO
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+from tqdm import tqdm
+
+# === CONFIG ===
+csv_path       = r"runs\detect\train\results.csv"
+images_dir     = r"runs\detect\train"
+MODEL_PATH     = "runs/detect/train/weights/best.pt"
+VAL_IMG_DIR    = Path("C:/Users/rageb/Desktop/new yolo model/datasets/valid_filtered/images")
+VAL_LABEL_DIR  = Path("C:/Users/rageb/Desktop/new yolo model/datasets/valid_filtered/labels")
+CLASS_NAMES    = ['car', 'emv', 'htv']
+IOU_THRESH     = 0.5
+CONF_THRESH    = 0.25
+SAVE_DIR       = Path("metrics")
+SAVE_DIR.mkdir(exist_ok=True)
+
+# === Load YOLO model ===
+model = YOLO(MODEL_PATH)
+
+# === Helpers ===
+def xywh2xyxy(xc, yc, w, h, img_w, img_h):
+    x1 = (xc - w/2) * img_w
+    y1 = (yc - h/2) * img_h
+    x2 = (xc + w/2) * img_w
+    y2 = (yc + h/2) * img_h
+    return [x1, y1, x2, y2]
+
+def compute_iou(b1, b2):
+    xi1, yi1 = max(b1[0], b2[0]), max(b1[1], b2[1])
+    xi2, yi2 = min(b1[2], b2[2]), min(b1[3], b2[3])
+    inter_w, inter_h = max(0, xi2-xi1), max(0, yi2-yi1)
+    inter = inter_w * inter_h
+    area1 = (b1[2]-b1[0])*(b1[3]-b1[1])
+    area2 = (b2[2]-b2[0])*(b2[3]-b2[1])
+    union = area1 + area2 - inter
+    return inter/union if union > 0 else 0
+
+# === Confusion Matrix Generation ===
+num_classes = len(CLASS_NAMES)
+bg_idx = num_classes - 1
+y_true, y_pred = [], []
+
+for img_path in tqdm(list(VAL_IMG_DIR.rglob("*.jpg")), desc="Evaluating"):
+    img = cv2.imread(str(img_path))
+    h, w = img.shape[:2]
+
+    results = model(img, conf=CONF_THRESH)[0]
+    pred_boxes, pred_cls, pred_scores = [], [], []
+    if results.boxes is not None:
+        for box in results.boxes:
+            pred_boxes.append(box.xyxy.cpu().numpy()[0].tolist())
+            pred_cls.append(int(box.cls.cpu().numpy()[0]))
+            pred_scores.append(float(box.conf.cpu().numpy()[0]))
+
+    gt_file = VAL_LABEL_DIR / f"{img_path.stem}.txt"
+    if not gt_file.exists():
+        continue
+
+    gt_boxes, gt_cls = [], []
+    with open(gt_file) as f:
+        for line in f:
+            parts = line.strip().split()
+            if len(parts) < 5:
+                continue
+            cid = int(parts[0])
+            xc, yc, ww, hh = map(float, parts[1:5])
+            gt_cls.append(cid)
+            gt_boxes.append(xywh2xyxy(xc, yc, ww, hh, w, h))
+
+    matched_gt, matched_pred = set(), set()
+    preds = sorted(
+        zip(pred_boxes, pred_cls, pred_scores, range(len(pred_boxes))),
+        key=lambda x: x[2], reverse=True
+    )
+    for pb, pc, ps, pidx in preds:
+        best_i, best_iou = -1, 0.0
+        for gi, gb in enumerate(gt_boxes):
+            if gi in matched_gt:
+                continue
+            iou = compute_iou(pb, gb)
+            if iou > best_iou:
+                best_iou, best_i = iou, gi
+        if best_iou >= IOU_THRESH:
+            y_true.append(gt_cls[best_i])
+            y_pred.append(pc)
+            matched_gt.add(best_i)
+            matched_pred.add(pidx)
+
+    for pidx, pc in enumerate(pred_cls):
+        if pidx not in matched_pred:
+            y_true.append(bg_idx)
+            y_pred.append(pc)
+
+    for gi, gc in enumerate(gt_cls):
+        if gi not in matched_gt:
+            y_true.append(gc)
+            y_pred.append(bg_idx)
+
+cm = confusion_matrix(y_true, y_pred, labels=range(num_classes))
+cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
+disp = ConfusionMatrixDisplay(cm_norm, display_labels=CLASS_NAMES)
+disp.plot(cmap=plt.cm.Blues)
+plt.title("Normalized Confusion Matrix")
+plt.savefig(SAVE_DIR / "confusion_matrix_normalized.png")
+plt.close()
+
+# === Plot Training/Validation Stats ===
+df = pd.read_csv(csv_path)
+df.columns = df.columns.str.strip()
+
+# Losses
+plt.figure(figsize=(12, 8))
+plt.plot(df["epoch"], df["train/box_loss"], label="Box Loss")
+plt.plot(df["epoch"], df["train/cls_loss"], label="Class Loss")
+plt.plot(df["epoch"], df["train/dfl_loss"], label="DFL Loss")
+plt.plot(df["epoch"], df["val/box_loss"], label="Val Box Loss")
+plt.plot(df["epoch"], df["val/cls_loss"], label="Val Class Loss")
+plt.plot(df["epoch"], df["val/dfl_loss"], label="Val DFL Loss")
+plt.title("Training and Validation Losses")
+plt.xlabel("Epoch")
+plt.ylabel("Loss")
+plt.legend()
+plt.grid(True)
+plt.savefig(SAVE_DIR / "losses_plot.png")
+plt.close()
+
+# Metrics
+plt.figure(figsize=(12, 8))
+plt.plot(df["epoch"], df["metrics/precision(B)"], label="Precision")
+plt.plot(df["epoch"], df["metrics/recall(B)"], label="Recall")
+plt.plot(df["epoch"], df["metrics/mAP50(B)"], label="mAP@0.5")
+plt.plot(df["epoch"], df["metrics/mAP50-95(B)"], label="mAP@0.5:0.95")
+plt.title("Eval Metrics")
+plt.xlabel("Epoch")
+plt.ylabel("Score")
+plt.legend()
+plt.grid(True)
+plt.savefig(SAVE_DIR / "metrics_plot.png")
+plt.close()
+
+# Learning Rates
+plt.figure(figsize=(12, 8))
+plt.plot(df["epoch"], df["lr/pg0"], label="lr/pg0")
+plt.plot(df["epoch"], df["lr/pg1"], label="lr/pg1")
+plt.plot(df["epoch"], df["lr/pg2"], label="lr/pg2")
+plt.title("Learning Rates")
+plt.xlabel("Epoch")
+plt.ylabel("LR")
+plt.legend()
+plt.grid(True)
+plt.savefig(SAVE_DIR / "learning_rates_plot.png")
+plt.close()
+
+# === Display Pre-generated Curves (if available) ===
+image_files = [
+    "F1_curve.png",
+    "P_curve_.png",
+    "P_curve.png",
+    "PR_curve.png",
+    "R_curve.png",
+]
+
+for filename in image_files:
+    src_path = os.path.join(images_dir, filename)
+    dst_path = SAVE_DIR / filename
+    if os.path.exists(src_path):
+        img = mpimg.imread(src_path)
+        plt.figure(figsize=(10, 8))
+        plt.imshow(img)
+        plt.axis('off')
+        plt.title(filename.replace(".png", "").replace("_", " ").title())
+        plt.savefig(dst_path)
+        plt.close()
+    else:
+        print(f"[Warning] {filename} not found in {images_dir}")

models/yolov8_custom.yaml

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+# Number of classes
+nc: 3
+
+# Model scaling factors for depth and width
+depth_multiple: 0.33  # scales layers like C2f repeats
+width_multiple: 0.50  # scales number of channels in Conv layers
+
+# ----------------------------------------
+# Backbone: Feature extraction layers
+# ----------------------------------------
+backbone:
+  [
+    [-1, 1, Conv, [64, 3, 2]],          # Layer 0: Conv -> 64 channels, stride 2
+    [-1, 1, Conv, [128, 3, 2]],         # Layer 1: Conv -> 128 channels, stride 2
+    [-1, 3, C2f, [128]],                # Layer 2: C2f -> 3 repeats, 128 channels
+    [-1, 1, Dropout, [0.1]],            # Layer 3: Dropout (10%)
+    [-1, 1, Conv, [256, 3, 2]],         # Layer 4: Conv -> 256 channels, downsample
+    [-1, 6, C2f, [256]],                # Layer 5: C2f -> 6 repeats, 256 channels
+    [-1, 1, Dropout, [0.1]],            # Layer 6: Dropout (10%)
+    [-1, 1, Conv, [320, 3, 1]],         # Layer 7: Conv -> 320 channels, stride 1 (no downsampling)
+  ]
+
+# ----------------------------------------
+# Head: Includes Neck + Detection layers
+# ----------------------------------------
+head:
+  [
+    # ---- Neck Begins ----
+    [-1, 1, Conv, [512, 3, 2]],         # Layer 8: Downsample + expand channels → Neck
+    [-1, 3, C2f, [512]],                # Layer 9: Feature fusion block → Neck
+    [-1, 1, Dropout, [0.1]],            # Layer 10: Regularization → Neck
+    [-1, 1, Conv, [768, 3, 1]],         # Layer 11: Increase depth without downsampling → Neck
+    # ---- Neck Ends ----
+
+    # ---- Detection Head Begins ----
+    [-1, 1, Conv, [1024, 3, 2]],        # Layer 12: Downsample for large object features
+    [-1, 3, C2f, [1024]],               # Layer 13: Final feature extraction
+    [-1, 1, Dropout, [0.1]],            # Layer 14: Dropout before SPP
+    [-1, 1, SPPF, [1024, 5]],           # Layer 15: Spatial Pyramid Pooling Fast (global context)
+    # ---- Detection Head Ends ----
+  ]

plots/plot.py

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+import cv2
+import numpy as np
+from pathlib import Path
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+
+from ultralytics import YOLO
+from sklearn.metrics import (
+    confusion_matrix, ConfusionMatrixDisplay,
+    precision_recall_curve, auc,
+    precision_recall_fscore_support
+)
+from sklearn.preprocessing import label_binarize
+from torch.utils.tensorboard import SummaryWriter
+
+# ─── CONFIG ────────────────────────────────────────────────────────────────────
+MODEL_PATH     = "runs/detect/train/weights/best.pt"
+VAL_IMG_DIR    = Path("C:/Users/rageb/Desktop/new yolo model/datasets/valid_filtered/images")
+VAL_LABEL_DIR  = Path("C:/Users/rageb/Desktop/new yolo model/datasets/valid_filtered/labels")
+CLASS_NAMES    = ['car', 'emv', 'htv']
+IOU_THRESH     = 0.5
+CONF_THRESH    = 0.25
+TB_LOG_DIR     = "runs/eval"
+# ────────────────────────────────────────────────────────────────────────────────
+
+num_classes = len(CLASS_NAMES)
+bg_idx = num_classes - 1  # index of “background”
+
+def xywh2xyxy(xc, yc, w, h, img_w, img_h):
+    """Convert YOLO normalized xc,yc,w,h → absolute x1,y1,x2,y2."""
+    x1 = (xc - w/2) * img_w
+    y1 = (yc - h/2) * img_h
+    x2 = (xc + w/2) * img_w
+    y2 = (yc + h/2) * img_h
+    return [x1, y1, x2, y2]
+
+def compute_iou(b1, b2):
+    """Compute IoU of two [x1,y1,x2,y2] boxes."""
+    xi1, yi1 = max(b1[0], b2[0]), max(b1[1], b2[1])
+    xi2, yi2 = min(b1[2], b2[2]), min(b1[3], b2[3])
+    inter_w, inter_h = max(0, xi2-xi1), max(0, yi2-yi1)
+    inter = inter_w * inter_h
+    area1 = (b1[2]-b1[0])*(b1[3]-b1[1])
+    area2 = (b2[2]-b2[0])*(b2[3]-b2[1])
+    union = area1 + area2 - inter
+    return inter/union if union>0 else 0
+
+# ─── Load model ────────────────────────────────────────────────────────────────
+model = YOLO(MODEL_PATH)
+
+# ─── Prepare holders ───────────────────────────────────────────────────────────
+y_true = []
+y_pred = []
+
+# ─── Loop over validation images ───────────────────────────────────────────────
+for img_path in tqdm(list(VAL_IMG_DIR.rglob("*.jpg")), desc="Evaluating"):
+    img = cv2.imread(str(img_path))
+    h, w = img.shape[:2]
+
+    # 1) Run inference
+    results = model(img, conf=CONF_THRESH)[0]
+    pred_boxes, pred_cls, pred_scores = [], [], []
+    if results.boxes is not None:
+        for box in results.boxes:
+            pred_boxes.append(box.xyxy.cpu().numpy()[0].tolist())
+            pred_cls.append(int(box.cls.cpu().numpy()[0]))
+            pred_scores.append(float(box.conf.cpu().numpy()[0]))
+
+    # 2) Load ground‑truth boxes
+    gt_file = VAL_LABEL_DIR / f"{img_path.stem}.txt"
+    if not gt_file.exists():
+        continue
+
+    gt_boxes, gt_cls = [], []
+    with open(gt_file) as f:
+        for line in f:
+            parts = line.strip().split()
+            if len(parts) < 5:
+                # skip malformed
+                continue
+            cid = int(parts[0])
+            xc, yc, ww, hh = map(float, parts[1:5])
+            gt_cls.append(cid)
+            gt_boxes.append(xywh2xyxy(xc, yc, ww, hh, w, h))
+
+    # 3) Match predictions → GT by descending score & IoU
+    matched_gt   = set()
+    matched_pred = set()
+    # pair (pb,pc,ps,pidx)
+    preds = sorted(
+        zip(pred_boxes, pred_cls, pred_scores, range(len(pred_boxes))),
+        key=lambda x: x[2], reverse=True
+    )
+    for pb, pc, ps, pidx in preds:
+        best_i, best_iou = -1, 0.0
+        for gi, gb in enumerate(gt_boxes):
+            if gi in matched_gt:
+                continue
+            iou = compute_iou(pb, gb)
+            if iou > best_iou:
+                best_iou, best_i = iou, gi
+        if best_iou >= IOU_THRESH:
+            # matched pair
+            y_true.append(gt_cls[best_i])
+            y_pred.append(pc)
+            matched_gt.add(best_i)
+            matched_pred.add(pidx)
+
+    # 4) Unmatched predictions → false positives (background → pred_class)
+    for pidx, pc in enumerate(pred_cls):
+        if pidx not in matched_pred:
+            y_true.append(bg_idx)
+            y_pred.append(pc)
+
+    # 5) Unmatched GT → false negatives (gt_class → background)
+    for gi, gc in enumerate(gt_cls):
+        if gi not in matched_gt:
+            y_true.append(gc)
+            y_pred.append(bg_idx)
+
+# ──�� Build & Save Confusion Matrix───────────────────────────────────────────
+cm = confusion_matrix(y_true, y_pred, labels=range(num_classes))
+cm_norm = cm.astype(float) / cm.sum(axis=1)[:,None]
+disp = ConfusionMatrixDisplay(cm_norm, display_labels=CLASS_NAMES)
+disp.plot(cmap=plt.cm.Blues)
+plt.title("Normalized Confusion Matrix")
+plt.savefig("confusion_matrix_normalized.png")
+plt.close()
+
+# ─── Precision–Recall Curves (per class) ───────────────────────────────────────
+y_true_bin = label_binarize(y_true, classes=range(num_classes))
+y_pred_bin = label_binarize(y_pred, classes=range(num_classes))
+
+plt.figure()
+for i in range(num_classes):
+    prec, rec, _ = precision_recall_curve(y_true_bin[:,i], y_pred_bin[:,i])
+    pr_auc = auc(rec, prec)
+    plt.plot(rec, prec, label=f"{CLASS_NAMES[i]} (AUC={pr_auc:.2f})")
+plt.xlabel("Recall"); plt.ylabel("Precision")
+plt.title("Precision–Recall Curves")
+plt.legend(loc="best")
+plt.savefig("PR_curve.png")
+plt.close()
+
+# ─── Per‑class Precision / Recall / F1 ────────────────────────────────────────
+prec, rec, f1, _ = precision_recall_fscore_support(
+    y_true, y_pred, labels=range(num_classes)
+)
+
+plt.figure()
+plt.plot(range(num_classes), prec, marker='o')
+plt.xticks(range(num_classes), CLASS_NAMES)
+plt.title("Precision per Class")
+plt.savefig("P_curve.png")
+plt.close()
+
+plt.figure()
+plt.plot(range(num_classes), rec, marker='o')
+plt.xticks(range(num_classes), CLASS_NAMES)
+plt.title("Recall per Class")
+plt.savefig("R_curve.png")
+plt.close()
+
+plt.figure()
+plt.plot(range(num_classes), f1, marker='o')
+plt.xticks(range(num_classes), CLASS_NAMES)
+plt.title("F1‑Score per Class")
+plt.savefig("Fl_curve.png")
+plt.close()
+
+# ─── Label Correlation & Distribution ─────────────────────────────────────────
+plt.figure()
+plt.imshow(cm, cmap='hot', interpolation='nearest')
+plt.colorbar()
+plt.xticks(range(num_classes), CLASS_NAMES)
+plt.yticks(range(num_classes), CLASS_NAMES)
+plt.title("Label Correlation Matrix")
+plt.savefig("labels_correlogram.jpg")
+plt.close()
+
+plt.figure()
+counts = np.bincount(y_true, minlength=num_classes)
+plt.bar(CLASS_NAMES, counts)
+plt.title("Label Distribution")
+plt.savefig("labels.jpg")
+plt.close()
+
+# ─── TensorBoard Logging ──────────────────────────────────────────────────────
+writer = SummaryWriter(TB_LOG_DIR)
+writer.add_scalar("Eval/Mean_Precision", np.mean(prec), 0)
+writer.add_scalar("Eval/Mean_Recall",    np.mean(rec),  0)
+writer.add_scalar("Eval/Mean_F1",        np.mean(f1),   0)
+writer.close()
+
+print("✅ Done! All plots saved and TensorBoard logs in", TB_LOG_DIR)

plots/results.csv

@@ -0,0 +1,101 @@
+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 3.3336,                 4.1731,                 4.1909,                0.34014,                0.14213,                0.00519,                0.00126,                 3.0029,                  4.749,                 4.0622,              0.0033274,              0.0033274,              0.0033274
+                      2,                 2.6338,                 3.2892,                 3.3215,                0.29353,                 0.3307,                 0.1717,                0.06593,                 2.0389,                 2.9526,                 2.8008,              0.0065948,              0.0065948,              0.0065948
+                      3,                 1.8366,                 2.3415,                 2.4096,                0.46484,                  0.381,                0.37024,                0.19317,                 1.7755,                 2.4902,                  2.448,              0.0097962,              0.0097962,              0.0097962
+                      4,                 1.5864,                 1.9187,                 2.0828,                0.45311,                0.48156,                0.41874,                0.25022,                 1.6394,                 2.2734,                 2.2256,               0.009703,               0.009703,               0.009703
+                      5,                 1.4359,                  1.645,                 1.9075,                0.61172,                0.59618,                0.60873,                0.36992,                 1.5192,                 1.6792,                 2.0703,               0.009604,               0.009604,               0.009604
+                      6,                 1.3686,                 1.5156,                 1.8167,                 0.6436,                0.60513,                0.63731,                0.40926,                 1.4454,                 1.5905,                 1.9867,               0.009505,               0.009505,               0.009505
+                      7,                 1.3222,                 1.4207,                 1.7587,                0.66845,                0.65711,                0.68417,                0.44975,                  1.358,                 1.3944,                 1.8511,               0.009406,               0.009406,               0.009406
+                      8,                 1.2678,                 1.3208,                 1.6952,                0.69374,                0.59167,                0.63799,                0.41885,                 1.3259,                 1.6184,                 1.8417,               0.009307,               0.009307,               0.009307
+                      9,                 1.2308,                 1.2796,                 1.6698,                0.65256,                0.63404,                0.67867,                0.43437,                 1.4128,                 1.5767,                 1.9333,               0.009208,               0.009208,               0.009208
+                     10,                 1.1947,                 1.2135,                 1.6261,                0.69865,                0.67123,                0.73074,                0.50189,                 1.2232,                 1.1844,                  1.702,               0.009109,               0.009109,               0.009109
+                     11,                 1.1763,                 1.1651,                 1.6062,                0.73488,                0.66802,                0.75458,                0.52332,                 1.2244,                 1.1336,                 1.7022,                0.00901,                0.00901,                0.00901
+                     12,                 1.1489,                 1.1342,                 1.5803,                0.65681,                0.71278,                0.74485,                0.51143,                  1.261,                  1.265,                 1.7295,               0.008911,               0.008911,               0.008911
+                     13,                 1.1339,                 1.0985,                 1.5629,                0.72488,                0.68812,                0.75271,                0.52328,                 1.2012,                 1.1653,                 1.6558,               0.008812,               0.008812,               0.008812
+                     14,                 1.1139,                 1.0772,                 1.5453,                 0.7206,                0.71843,                0.77325,                0.53771,                 1.2034,                 1.1192,                 1.6789,               0.008713,               0.008713,               0.008713
+                     15,                 1.0954,                 1.0458,                 1.5296,                0.68214,                0.70276,                0.75323,                0.52616,                  1.181,                 1.2829,                 1.6459,               0.008614,               0.008614,               0.008614
+                     16,                 1.0796,                 1.0111,                 1.5065,                0.67223,                0.74373,                0.75556,                0.52373,                 1.1931,                 1.2028,                 1.6536,               0.008515,               0.008515,               0.008515
+                     17,                 1.0655,                0.98774,                 1.4987,                0.79118,                0.74162,                 0.8143,                0.57488,                 1.1569,                0.93911,                 1.6269,               0.008416,               0.008416,               0.008416
+                     18,                  1.055,                0.98353,                 1.4911,                0.74042,                0.75502,                0.80163,                0.56243,                 1.1943,                 1.0309,                 1.6726,               0.008317,               0.008317,               0.008317
+                     19,                 1.0406,                0.95731,                  1.478,                0.76636,                0.75063,                0.79669,                0.56345,                 1.1479,                 0.9808,                 1.6047,               0.008218,               0.008218,               0.008218
+                     20,                 1.0411,                0.94119,                  1.469,                0.76687,                0.75724,                0.81671,                0.58533,                 1.1286,                0.94393,                 1.5843,               0.008119,               0.008119,               0.008119
+                     21,                 1.0244,                0.92019,                 1.4502,                0.74114,                0.72371,                0.80381,                0.57849,                 1.1087,                 1.0263,                 1.5707,                0.00802,                0.00802,                0.00802
+                     22,                 1.0181,                0.91716,                 1.4465,                0.78932,                0.74232,                0.81173,                0.58104,                 1.1114,                 0.9444,                 1.5716,               0.007921,               0.007921,               0.007921
+                     23,                0.99663,                0.89298,                 1.4294,                0.78786,                0.75902,                0.81926,                0.59603,                 1.0998,                0.90816,                 1.5637,               0.007822,               0.007822,               0.007822
+                     24,                 1.0058,                0.88515,                 1.4285,                0.82598,                0.73038,                0.82398,                0.59432,                 1.1001,                0.93526,                  1.554,               0.007723,               0.007723,               0.007723
+                     25,                0.98731,                0.86355,                 1.4115,                0.79641,                0.74273,                0.82705,                0.60472,                 1.0996,                0.90858,                  1.537,               0.007624,               0.007624,               0.007624
+                     26,                0.97273,                0.85405,                 1.4021,                0.81536,                0.75402,                 0.8237,                0.59685,                 1.0945,                0.87556,                 1.5687,               0.007525,               0.007525,               0.007525
+                     27,                0.95927,                0.82781,                 1.3895,                0.77213,                0.78374,                0.83304,                0.60803,                 1.0708,                0.84085,                 1.5418,               0.007426,               0.007426,               0.007426
+                     28,                0.95449,                  0.823,                 1.3822,                0.82268,                0.76908,                0.83612,                0.60935,                 1.0803,                0.84244,                 1.5199,               0.007327,               0.007327,               0.007327
+                     29,                0.96206,                 0.8206,                 1.3821,                0.80113,                0.76897,                0.83466,                0.61138,                 1.0732,                0.85333,                 1.5192,               0.007228,               0.007228,               0.007228
+                     30,                 0.9489,                0.80558,                 1.3732,                0.83973,                 0.7698,                0.84453,                0.61705,                 1.0693,                0.81017,                 1.5144,               0.007129,               0.007129,               0.007129
+                     31,                0.93628,                0.78536,                 1.3596,                0.85978,                0.76836,                0.85737,                0.62385,                 1.0643,                0.77095,                 1.5045,                0.00703,                0.00703,                0.00703
+                     32,                0.93888,                0.78808,                  1.364,                0.85181,                0.77211,                0.85251,                0.62559,                 1.0626,                0.78029,                 1.4991,               0.006931,               0.006931,               0.006931
+                     33,                0.91903,                0.76072,                 1.3485,                0.79604,                0.79913,                0.84882,                0.62046,                 1.0515,                0.79757,                 1.4834,               0.006832,               0.006832,               0.006832
+                     34,                0.91528,                0.75934,                 1.3442,                0.81344,                0.77562,                0.84899,                0.62579,                  1.047,                0.81286,                 1.4891,               0.006733,               0.006733,               0.006733
+                     35,                0.91243,                 0.7574,                 1.3388,                0.81548,                0.79836,                0.85066,                0.63004,                  1.034,                0.78111,                   1.48,               0.006634,               0.006634,               0.006634
+                     36,                0.90512,                0.74518,                  1.332,                0.84917,                0.79805,                  0.864,                0.64242,                 1.0247,                0.73889,                 1.4645,               0.006535,               0.006535,               0.006535
+                     37,                0.89264,                0.72857,                 1.3244,                0.83009,                0.80038,                0.85598,                0.63618,                 1.0212,                0.73398,                 1.4803,               0.006436,               0.006436,               0.006436
+                     38,                0.89167,                0.72835,                  1.325,                0.82482,                0.77298,                0.85377,                0.63553,                 1.0232,                0.76677,                 1.4524,               0.006337,               0.006337,               0.006337
+                     39,                0.87797,                0.71598,                 1.3141,                0.78841,                0.81306,                0.84744,                0.62925,                 1.0334,                0.76659,                 1.4679,               0.006238,               0.006238,               0.006238
+                     40,                0.88045,                0.71592,                 1.3136,                0.82978,                0.79808,                0.85082,                0.63345,                 1.0256,                0.73113,                 1.4668,               0.006139,               0.006139,               0.006139
+                     41,                0.87191,                0.70092,                 1.3054,                0.82146,                0.78728,                0.85456,                0.63944,                 1.0197,                0.77223,                 1.4588,                0.00604,                0.00604,                0.00604
+                     42,                0.87027,                0.70284,                 1.3048,                0.84225,                0.79814,                 0.8666,                0.63724,                 1.0201,                0.72969,                 1.4541,               0.005941,               0.005941,               0.005941
+                     43,                0.86255,                0.69955,                 1.2952,                0.83711,                0.78665,                0.85414,                0.63115,                 1.0128,                0.73627,                 1.4542,               0.005842,               0.005842,               0.005842
+                     44,                0.85459,                 0.6891,                 1.2862,                 0.8145,                0.80616,                0.85329,                0.63614,                 1.0109,                0.73742,                 1.4551,               0.005743,               0.005743,               0.005743
+                     45,                0.84504,                0.67492,                 1.2812,                0.83364,                0.79951,                0.86182,                0.64374,                 1.0109,                0.70803,                 1.4477,               0.005644,               0.005644,               0.005644
+                     46,                0.84006,                0.66895,                 1.2832,                0.84941,                0.77407,                0.86216,                0.64349,                 1.0127,                0.71599,                 1.4571,               0.005545,               0.005545,               0.005545
+                     47,                0.84117,                0.66283,                 1.2718,                0.83615,                0.81162,                0.85221,                0.63774,                 1.0039,                0.72896,                 1.4528,               0.005446,               0.005446,               0.005446
+                     48,                0.83692,                 0.6616,                 1.2741,                0.84533,                0.78987,                0.85715,                0.64179,                 1.0055,                0.71898,                  1.443,               0.005347,               0.005347,               0.005347
+                     49,                0.83891,                 0.6491,                 1.2724,                0.84703,                0.78753,                0.85297,                0.64227,                0.99378,                 0.7195,                 1.4386,               0.005248,               0.005248,               0.005248
+                     50,                0.82881,                0.64724,                   1.26,                0.85574,                0.79643,                 0.8608,                 0.6438,                0.99969,                0.70979,                 1.4361,               0.005149,               0.005149,               0.005149
+                     51,                0.81264,                0.63274,                 1.2505,                0.85736,                    0.8,                0.85662,                0.64449,                0.99606,                0.70714,                 1.4336,                0.00505,                0.00505,                0.00505
+                     52,                 0.8166,                0.63594,                 1.2534,                0.84197,                 0.8183,                0.85554,                0.64408,                0.98752,                0.68258,                 1.4194,               0.004951,               0.004951,               0.004951
+                     53,                0.81005,                0.62891,                 1.2502,                 0.8496,                0.81907,                0.86511,                0.64729,                 1.0108,                0.69152,                 1.4428,               0.004852,               0.004852,               0.004852
+                     54,                0.80082,                0.62357,                 1.2435,                0.85348,                0.81126,                0.86288,                0.64455,                 1.0122,                0.69784,                 1.4361,               0.004753,               0.004753,               0.004753
+                     55,                0.80628,                0.62095,                 1.2489,                 0.8355,                 0.8243,                0.85502,                0.63636,                 1.0109,                0.72068,                 1.4427,               0.004654,               0.004654,               0.004654
+                     56,                0.79864,                0.61679,                 1.2394,                0.85062,                0.78732,                0.85909,                0.64558,                 1.0018,                0.71647,                 1.4384,               0.004555,               0.004555,               0.004555
+                     57,                 0.7856,                0.60721,                  1.233,                0.82226,                0.80409,                0.84956,                0.63818,                 1.0062,                0.73255,                 1.4511,               0.004456,               0.004456,               0.004456
+                     58,                0.78324,                0.60197,                 1.2329,                 0.8405,                0.81376,                0.85031,                0.63892,                0.99674,                0.71787,                 1.4487,               0.004357,               0.004357,               0.004357
+                     59,                0.78162,                0.59455,                   1.23,                0.81313,                0.82212,                0.85331,                0.64553,                 1.0054,                0.73082,                 1.4462,               0.004258,               0.004258,               0.004258
+                     60,                0.77118,                0.58714,                 1.2203,                0.81894,                0.81789,                0.84359,                0.63038,                 1.0095,                0.74069,                 1.4542,               0.004159,               0.004159,               0.004159
+                     61,                0.76692,                0.58105,                 1.2131,                0.83985,                0.80045,                0.85464,                0.63969,                 1.0085,                 0.7203,                 1.4535,                0.00406,                0.00406,                0.00406
+                     62,                0.76564,                 0.5848,                 1.2167,                0.84251,                0.78865,                0.85366,                0.63863,                 1.0079,                0.74339,                 1.4472,               0.003961,               0.003961,               0.003961
+                     63,                0.76683,                0.58464,                 1.2124,                0.83407,                0.79956,                0.85758,                0.64303,                0.99768,                0.72517,                 1.4376,               0.003862,               0.003862,               0.003862
+                     64,                0.76003,                 0.5777,                 1.2074,                0.82169,                0.81987,                0.85521,                0.63797,                0.99668,                0.72377,                 1.4348,               0.003763,               0.003763,               0.003763
+                     65,                0.75427,                0.56831,                 1.1974,                0.82961,                0.81766,                0.85525,                0.64145,                0.99921,                0.72727,                 1.4388,               0.003664,               0.003664,               0.003664
+                     66,                0.74364,                0.56468,                  1.198,                0.83326,                0.81998,                0.86021,                0.64672,                0.99524,                 0.7189,                 1.4454,               0.003565,               0.003565,               0.003565
+                     67,                0.74233,                0.55644,                 1.1948,                0.84516,                0.79372,                0.85971,                0.64796,                0.99724,                0.71786,                 1.4526,               0.003466,               0.003466,               0.003466
+                     68,                0.74146,                0.55233,                  1.194,                0.83255,                0.80268,                  0.852,                0.64119,                 1.0017,                 0.7344,                 1.4553,               0.003367,               0.003367,               0.003367
+                     69,                0.73564,                0.55572,                 1.1943,                0.84312,                0.79368,                0.85093,                0.64026,                 0.9952,                0.74642,                 1.4488,               0.003268,               0.003268,               0.003268
+                     70,                 0.7286,                0.54735,                 1.1864,                0.84544,                0.79686,                0.85512,                0.64517,                0.99592,                0.73067,                 1.4497,               0.003169,               0.003169,               0.003169
+                     71,                0.72836,                0.54709,                 1.1845,                0.85079,                0.79342,                0.85073,                0.63655,                 1.0026,                0.72754,                 1.4531,                0.00307,                0.00307,                0.00307
+                     72,                0.72415,                0.53604,                 1.1772,                0.83478,                0.80206,                0.85285,                0.63909,                 1.0036,                0.73827,                 1.4497,               0.002971,               0.002971,               0.002971
+                     73,                0.71854,                0.52938,                 1.1767,                0.83605,                0.80299,                0.85428,                 0.6407,                 1.0012,                0.73531,                 1.4482,               0.002872,               0.002872,               0.002872
+                     74,                0.71198,                0.52784,                 1.1733,                 0.8293,                0.81683,                0.85254,                0.64083,                 0.9993,                0.73292,                 1.4521,               0.002773,               0.002773,               0.002773
+                     75,                0.71043,                0.52836,                 1.1736,                 0.8243,                 0.8192,                0.85427,                0.64084,                0.99359,                0.73727,                 1.4446,               0.002674,               0.002674,               0.002674
+                     76,                0.70855,                0.52491,                 1.1678,                0.82966,                0.81019,                0.85529,                 0.6401,                0.99636,                0.72477,                 1.4457,               0.002575,               0.002575,               0.002575
+                     77,                0.70137,                0.51911,                  1.165,                0.85136,                0.79125,                0.85664,                0.64164,                0.99352,                 0.7251,                 1.4424,               0.002476,               0.002476,               0.002476
+                     78,                0.69108,                0.51054,                 1.1611,                0.83181,                0.80943,                0.85423,                0.63931,                0.99541,                0.73227,                 1.4455,               0.002377,               0.002377,               0.002377
+                     79,                0.69708,                0.51066,                 1.1575,                0.83981,                0.80217,                0.85692,                0.64159,                0.99558,                0.72758,                 1.4474,               0.002278,               0.002278,               0.002278
+                     80,                0.68296,                0.50331,                 1.1546,                0.86249,                0.78357,                0.85448,                0.64076,                0.99297,                0.73278,                 1.4433,               0.002179,               0.002179,               0.002179
+                     81,                0.68314,                0.50144,                 1.1534,                 0.8703,                0.78546,                 0.8567,                0.64236,                0.99344,                0.73131,                 1.4457,                0.00208,                0.00208,                0.00208
+                     82,                0.67592,                0.49407,                 1.1436,                0.86134,                0.78397,                 0.8551,                0.64288,                0.99447,                0.72298,                 1.4474,               0.001981,               0.001981,               0.001981
+                     83,                0.67502,                0.49456,                 1.1456,                0.87916,                0.77147,                0.85645,                0.64302,                0.99494,                0.72201,                   1.45,               0.001882,               0.001882,               0.001882
+                     84,                0.67722,                0.49158,                  1.144,                 0.8658,                0.77878,                0.85409,                0.64162,                0.99431,                0.72672,                 1.4488,               0.001783,               0.001783,               0.001783
+                     85,                0.66452,                0.48692,                 1.1403,                0.80975,                0.82892,                 0.8562,                0.64409,                0.99633,                0.72083,                 1.4533,               0.001684,               0.001684,               0.001684
+                     86,                0.66367,                0.48178,                 1.1367,                 0.8675,                0.78473,                0.85657,                0.64238,                0.99468,                0.71907,                 1.4538,               0.001585,               0.001585,               0.001585
+                     87,                0.65674,                0.47763,                 1.1317,                0.84138,                0.80839,                0.85903,                0.64489,                0.99545,                0.71732,                 1.4542,               0.001486,               0.001486,               0.001486
+                     88,                0.65159,                0.47077,                 1.1305,                0.83909,                 0.8186,                0.85772,                0.64519,                0.99463,                0.71388,                 1.4549,               0.001387,               0.001387,               0.001387
+                     89,                0.65194,                0.47269,                 1.1332,                0.84512,                0.80954,                0.85706,                0.64325,                0.99293,                0.71527,                 1.4551,               0.001288,               0.001288,               0.001288
+                     90,                0.64748,                0.46937,                 1.1257,                0.84756,                0.80888,                0.85751,                 0.6447,                 0.9903,                0.71736,                 1.4541,               0.001189,               0.001189,               0.001189
+                     91,                0.57498,                0.36088,                 1.0641,                 0.8741,                0.78525,                0.85701,                0.64552,                0.99222,                0.72113,                 1.4579,                0.00109,                0.00109,                0.00109
+                     92,                0.56454,                0.34877,                 1.0588,                0.88319,                 0.7786,                0.85531,                0.64413,                0.99359,                 0.7244,                 1.4596,               0.000991,               0.000991,               0.000991
+                     93,                0.55534,                0.34107,                 1.0495,                0.88446,                0.78242,                0.85518,                0.64334,                0.99588,                0.72836,                 1.4628,               0.000892,               0.000892,               0.000892
+                     94,                0.54233,                 0.3361,                 1.0421,                0.88369,                0.78092,                0.85356,                0.64125,                0.99696,                0.73288,                 1.4636,               0.000793,               0.000793,               0.000793
+                     95,                0.53972,                 0.3306,                 1.0382,                  0.879,                0.77972,                0.85262,                0.63997,                  0.999,                0.73394,                 1.4661,               0.000694,               0.000694,               0.000694
+                     96,                0.53002,                0.32325,                 1.0315,                0.87436,                0.78204,                0.85128,                0.63967,                  1.001,                0.74033,                 1.4694,               0.000595,               0.000595,               0.000595
+                     97,                0.52823,                0.32332,                 1.0277,                0.87592,                0.78263,                   0.85,                0.63856,                 1.0022,                0.74553,                 1.4714,               0.000496,               0.000496,               0.000496
+                     98,                0.52277,                0.32193,                 1.0251,                0.87743,                0.78224,                0.84978,                0.63888,                  1.004,                0.74746,                 1.4749,               0.000397,               0.000397,               0.000397
+                     99,                0.51731,                0.31577,                 1.0235,                0.87748,                0.78619,                0.84878,                0.63811,                 1.0045,                0.75104,                 1.4773,               0.000298,               0.000298,               0.000298
+                    100,                0.51285,                0.31644,                  1.021,                 0.8807,                0.78446,                0.84859,                0.63742,                 1.0051,                0.75515,                 1.4791,               0.000199,               0.000199,               0.000199

requirements.txt

@@ -0,0 +1,5 @@
+ultralytics
+opencv-python
+torch
+numpy
+matplotlib

runs/detect/train/args.yaml

@@ -0,0 +1,107 @@
+task: detect
+mode: train
+model: yolov8s.yaml
+data: datasets/data.yaml
+epochs: 100
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: cuda
+workers: 0
+project: null
+name: train
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: 4
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: null
+tracker: botsort.yaml
+save_dir: runs\detect\train

runs/detect/train/results.csv

@@ -0,0 +1,101 @@
+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 3.3336,                 4.1731,                 4.1909,                0.34014,                0.14213,                0.00519,                0.00126,                 3.0029,                  4.749,                 4.0622,              0.0033274,              0.0033274,              0.0033274
+                      2,                 2.6338,                 3.2892,                 3.3215,                0.29353,                 0.3307,                 0.1717,                0.06593,                 2.0389,                 2.9526,                 2.8008,              0.0065948,              0.0065948,              0.0065948
+                      3,                 1.8366,                 2.3415,                 2.4096,                0.46484,                  0.381,                0.37024,                0.19317,                 1.7755,                 2.4902,                  2.448,              0.0097962,              0.0097962,              0.0097962
+                      4,                 1.5864,                 1.9187,                 2.0828,                0.45311,                0.48156,                0.41874,                0.25022,                 1.6394,                 2.2734,                 2.2256,               0.009703,               0.009703,               0.009703
+                      5,                 1.4359,                  1.645,                 1.9075,                0.61172,                0.59618,                0.60873,                0.36992,                 1.5192,                 1.6792,                 2.0703,               0.009604,               0.009604,               0.009604
+                      6,                 1.3686,                 1.5156,                 1.8167,                 0.6436,                0.60513,                0.63731,                0.40926,                 1.4454,                 1.5905,                 1.9867,               0.009505,               0.009505,               0.009505
+                      7,                 1.3222,                 1.4207,                 1.7587,                0.66845,                0.65711,                0.68417,                0.44975,                  1.358,                 1.3944,                 1.8511,               0.009406,               0.009406,               0.009406
+                      8,                 1.2678,                 1.3208,                 1.6952,                0.69374,                0.59167,                0.63799,                0.41885,                 1.3259,                 1.6184,                 1.8417,               0.009307,               0.009307,               0.009307
+                      9,                 1.2308,                 1.2796,                 1.6698,                0.65256,                0.63404,                0.67867,                0.43437,                 1.4128,                 1.5767,                 1.9333,               0.009208,               0.009208,               0.009208
+                     10,                 1.1947,                 1.2135,                 1.6261,                0.69865,                0.67123,                0.73074,                0.50189,                 1.2232,                 1.1844,                  1.702,               0.009109,               0.009109,               0.009109
+                     11,                 1.1763,                 1.1651,                 1.6062,                0.73488,                0.66802,                0.75458,                0.52332,                 1.2244,                 1.1336,                 1.7022,                0.00901,                0.00901,                0.00901
+                     12,                 1.1489,                 1.1342,                 1.5803,                0.65681,                0.71278,                0.74485,                0.51143,                  1.261,                  1.265,                 1.7295,               0.008911,               0.008911,               0.008911
+                     13,                 1.1339,                 1.0985,                 1.5629,                0.72488,                0.68812,                0.75271,                0.52328,                 1.2012,                 1.1653,                 1.6558,               0.008812,               0.008812,               0.008812
+                     14,                 1.1139,                 1.0772,                 1.5453,                 0.7206,                0.71843,                0.77325,                0.53771,                 1.2034,                 1.1192,                 1.6789,               0.008713,               0.008713,               0.008713
+                     15,                 1.0954,                 1.0458,                 1.5296,                0.68214,                0.70276,                0.75323,                0.52616,                  1.181,                 1.2829,                 1.6459,               0.008614,               0.008614,               0.008614
+                     16,                 1.0796,                 1.0111,                 1.5065,                0.67223,                0.74373,                0.75556,                0.52373,                 1.1931,                 1.2028,                 1.6536,               0.008515,               0.008515,               0.008515
+                     17,                 1.0655,                0.98774,                 1.4987,                0.79118,                0.74162,                 0.8143,                0.57488,                 1.1569,                0.93911,                 1.6269,               0.008416,               0.008416,               0.008416
+                     18,                  1.055,                0.98353,                 1.4911,                0.74042,                0.75502,                0.80163,                0.56243,                 1.1943,                 1.0309,                 1.6726,               0.008317,               0.008317,               0.008317
+                     19,                 1.0406,                0.95731,                  1.478,                0.76636,                0.75063,                0.79669,                0.56345,                 1.1479,                 0.9808,                 1.6047,               0.008218,               0.008218,               0.008218
+                     20,                 1.0411,                0.94119,                  1.469,                0.76687,                0.75724,                0.81671,                0.58533,                 1.1286,                0.94393,                 1.5843,               0.008119,               0.008119,               0.008119
+                     21,                 1.0244,                0.92019,                 1.4502,                0.74114,                0.72371,                0.80381,                0.57849,                 1.1087,                 1.0263,                 1.5707,                0.00802,                0.00802,                0.00802
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+                     23,                0.99663,                0.89298,                 1.4294,                0.78786,                0.75902,                0.81926,                0.59603,                 1.0998,                0.90816,                 1.5637,               0.007822,               0.007822,               0.007822
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+                     30,                 0.9489,                0.80558,                 1.3732,                0.83973,                 0.7698,                0.84453,                0.61705,                 1.0693,                0.81017,                 1.5144,               0.007129,               0.007129,               0.007129
+                     31,                0.93628,                0.78536,                 1.3596,                0.85978,                0.76836,                0.85737,                0.62385,                 1.0643,                0.77095,                 1.5045,                0.00703,                0.00703,                0.00703
+                     32,                0.93888,                0.78808,                  1.364,                0.85181,                0.77211,                0.85251,                0.62559,                 1.0626,                0.78029,                 1.4991,               0.006931,               0.006931,               0.006931
+                     33,                0.91903,                0.76072,                 1.3485,                0.79604,                0.79913,                0.84882,                0.62046,                 1.0515,                0.79757,                 1.4834,               0.006832,               0.006832,               0.006832
+                     34,                0.91528,                0.75934,                 1.3442,                0.81344,                0.77562,                0.84899,                0.62579,                  1.047,                0.81286,                 1.4891,               0.006733,               0.006733,               0.006733
+                     35,                0.91243,                 0.7574,                 1.3388,                0.81548,                0.79836,                0.85066,                0.63004,                  1.034,                0.78111,                   1.48,               0.006634,               0.006634,               0.006634
+                     36,                0.90512,                0.74518,                  1.332,                0.84917,                0.79805,                  0.864,                0.64242,                 1.0247,                0.73889,                 1.4645,               0.006535,               0.006535,               0.006535
+                     37,                0.89264,                0.72857,                 1.3244,                0.83009,                0.80038,                0.85598,                0.63618,                 1.0212,                0.73398,                 1.4803,               0.006436,               0.006436,               0.006436
+                     38,                0.89167,                0.72835,                  1.325,                0.82482,                0.77298,                0.85377,                0.63553,                 1.0232,                0.76677,                 1.4524,               0.006337,               0.006337,               0.006337
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+                     43,                0.86255,                0.69955,                 1.2952,                0.83711,                0.78665,                0.85414,                0.63115,                 1.0128,                0.73627,                 1.4542,               0.005842,               0.005842,               0.005842
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+                     45,                0.84504,                0.67492,                 1.2812,                0.83364,                0.79951,                0.86182,                0.64374,                 1.0109,                0.70803,                 1.4477,               0.005644,               0.005644,               0.005644
+                     46,                0.84006,                0.66895,                 1.2832,                0.84941,                0.77407,                0.86216,                0.64349,                 1.0127,                0.71599,                 1.4571,               0.005545,               0.005545,               0.005545
+                     47,                0.84117,                0.66283,                 1.2718,                0.83615,                0.81162,                0.85221,                0.63774,                 1.0039,                0.72896,                 1.4528,               0.005446,               0.005446,               0.005446
+                     48,                0.83692,                 0.6616,                 1.2741,                0.84533,                0.78987,                0.85715,                0.64179,                 1.0055,                0.71898,                  1.443,               0.005347,               0.005347,               0.005347
+                     49,                0.83891,                 0.6491,                 1.2724,                0.84703,                0.78753,                0.85297,                0.64227,                0.99378,                 0.7195,                 1.4386,               0.005248,               0.005248,               0.005248
+                     50,                0.82881,                0.64724,                   1.26,                0.85574,                0.79643,                 0.8608,                 0.6438,                0.99969,                0.70979,                 1.4361,               0.005149,               0.005149,               0.005149
+                     51,                0.81264,                0.63274,                 1.2505,                0.85736,                    0.8,                0.85662,                0.64449,                0.99606,                0.70714,                 1.4336,                0.00505,                0.00505,                0.00505
+                     52,                 0.8166,                0.63594,                 1.2534,                0.84197,                 0.8183,                0.85554,                0.64408,                0.98752,                0.68258,                 1.4194,               0.004951,               0.004951,               0.004951
+                     53,                0.81005,                0.62891,                 1.2502,                 0.8496,                0.81907,                0.86511,                0.64729,                 1.0108,                0.69152,                 1.4428,               0.004852,               0.004852,               0.004852
+                     54,                0.80082,                0.62357,                 1.2435,                0.85348,                0.81126,                0.86288,                0.64455,                 1.0122,                0.69784,                 1.4361,               0.004753,               0.004753,               0.004753
+                     55,                0.80628,                0.62095,                 1.2489,                 0.8355,                 0.8243,                0.85502,                0.63636,                 1.0109,                0.72068,                 1.4427,               0.004654,               0.004654,               0.004654
+                     56,                0.79864,                0.61679,                 1.2394,                0.85062,                0.78732,                0.85909,                0.64558,                 1.0018,                0.71647,                 1.4384,               0.004555,               0.004555,               0.004555
+                     57,                 0.7856,                0.60721,                  1.233,                0.82226,                0.80409,                0.84956,                0.63818,                 1.0062,                0.73255,                 1.4511,               0.004456,               0.004456,               0.004456
+                     58,                0.78324,                0.60197,                 1.2329,                 0.8405,                0.81376,                0.85031,                0.63892,                0.99674,                0.71787,                 1.4487,               0.004357,               0.004357,               0.004357
+                     59,                0.78162,                0.59455,                   1.23,                0.81313,                0.82212,                0.85331,                0.64553,                 1.0054,                0.73082,                 1.4462,               0.004258,               0.004258,               0.004258
+                     60,                0.77118,                0.58714,                 1.2203,                0.81894,                0.81789,                0.84359,                0.63038,                 1.0095,                0.74069,                 1.4542,               0.004159,               0.004159,               0.004159
+                     61,                0.76692,                0.58105,                 1.2131,                0.83985,                0.80045,                0.85464,                0.63969,                 1.0085,                 0.7203,                 1.4535,                0.00406,                0.00406,                0.00406
+                     62,                0.76564,                 0.5848,                 1.2167,                0.84251,                0.78865,                0.85366,                0.63863,                 1.0079,                0.74339,                 1.4472,               0.003961,               0.003961,               0.003961
+                     63,                0.76683,                0.58464,                 1.2124,                0.83407,                0.79956,                0.85758,                0.64303,                0.99768,                0.72517,                 1.4376,               0.003862,               0.003862,               0.003862
+                     64,                0.76003,                 0.5777,                 1.2074,                0.82169,                0.81987,                0.85521,                0.63797,                0.99668,                0.72377,                 1.4348,               0.003763,               0.003763,               0.003763
+                     65,                0.75427,                0.56831,                 1.1974,                0.82961,                0.81766,                0.85525,                0.64145,                0.99921,                0.72727,                 1.4388,               0.003664,               0.003664,               0.003664
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