notebooks/hyperparameter_tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aaalexlit/hacking-human-vasculature/blob/main/notebooks/hyperparameter_tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "uTVOM8xNZ5-L"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!git clone https://github.com/aaalexlit/hacking-human-vasculature.git\n",
        "!pip install ultralytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "LF1WiLpbGlxr"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from pathlib import Path\n",
        "cur_path = Path.cwd()\n",
        "os.environ['YOLO_CONFIG_DIR'] = str(cur_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDkqOixELaCB",
        "outputId": "6c7397c0-bd1d-4ce1-9923-c524b742ef52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing /content/settings.yaml\n"
          ]
        }
      ],
      "source": [
        "%%writefile /content/settings.yaml\n",
        "settings_version: 0.0.4\n",
        "datasets_dir: /content\n",
        "weights_dir: weights\n",
        "runs_dir: runs\n",
        "uuid: 569f3ba64b326db489132663f79cd37279811de477381b83ac131e6cdd129cbb\n",
        "sync: true\n",
        "api_key: ''\n",
        "openai_api_key: ''\n",
        "clearml: true\n",
        "comet: true\n",
        "dvc: true\n",
        "hub: true\n",
        "mlflow: true\n",
        "neptune: true\n",
        "raytune: true\n",
        "tensorboard: true\n",
        "wandb: true\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "hemmB7vW__G4"
      },
      "outputs": [],
      "source": [
        "import yaml\n",
        "train_path = 'train'\n",
        "val_path = 'val'\n",
        "test_path = 'test'\n",
        "dataset_path = './hacking-human-vasculature/dataset'\n",
        "names = {0: 'Vessel'}\n",
        "yaml_path = 'yolo_dataset.yaml'\n",
        "\n",
        "# Create a dictionary with the required content\n",
        "yaml_data = {\n",
        "    'names': names,\n",
        "    'nc': len(names),\n",
        "    'test': test_path,\n",
        "    'train': train_path,\n",
        "    'val': val_path,\n",
        "    'path': dataset_path\n",
        "}\n",
        "\n",
        "# Write the dictionary to a YAML file\n",
        "with open(yaml_path, 'w') as file:\n",
        "    yaml.dump(yaml_data, file, default_flow_style=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Z8AY2ODiQLD4"
      },
      "outputs": [],
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "project = 'blood_vessel_segmentation'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JtNnzFGEAN9",
        "outputId": "a9ba977a-a1c0-40d7-85ab-35a3d3160cf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-seg.pt to 'yolov8n-seg.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6.73M/6.73M [00:00<00:00, 302MB/s]\n"
          ]
        }
      ],
      "source": [
        "model = YOLO('yolov8n-seg.pt')\n",
        "\n",
        "name = 'hyperparameter_tuning'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukfFGMpWhU9B",
        "outputId": "e3842cb6-a3f1-4b19-e8a3-cd0f8a6cffcb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mTuner: \u001b[0mInitialized Tuner instance with 'tune_dir=blood_vessel_segmentation/tune'\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m💡 Learn about tuning at https://docs.ultralytics.com/guides/hyperparameter-tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 1/30 with hyperparameters: {'lr0': 0.01, 'lrf': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'box': 7.5, 'cls': 0.5, 'dfl': 1.5, '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, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m1/30 iterations complete ✅ (228.95s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 2/30 with hyperparameters: {'lr0': 0.01053, 'lrf': 0.01041, 'momentum': 0.92417, 'weight_decay': 0.00045, 'warmup_epochs': 3.14624, 'warmup_momentum': 0.75105, 'box': 7.59361, 'cls': 0.48314, 'dfl': 1.49017, 'hsv_h': 0.01571, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.09621, 'scale': 0.50401, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.51794, 'mosaic': 0.99101, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m2/30 iterations complete ✅ (444.48s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 3/30 with hyperparameters: {'lr0': 0.00999, 'lrf': 0.01, 'momentum': 0.93663, 'weight_decay': 0.0005, 'warmup_epochs': 2.99874, 'warmup_momentum': 0.80074, 'box': 7.49179, 'cls': 0.5, 'dfl': 1.50424, 'hsv_h': 0.015, 'hsv_s': 0.70061, 'hsv_v': 0.40074, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.50006, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m3/30 iterations complete ✅ (660.06s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 4/30 with hyperparameters: {'lr0': 0.01, 'lrf': 0.00995, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 2.52091, 'warmup_momentum': 0.5775, 'box': 7.35184, 'cls': 0.50326, 'dfl': 1.48246, 'hsv_h': 0.01563, 'hsv_s': 0.7723, 'hsv_v': 0.44767, 'degrees': 0.0, 'translate': 0.10303, 'scale': 0.41786, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.52181, 'mosaic': 0.86684, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m4/30 iterations complete ✅ (875.25s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 5/30 with hyperparameters: {'lr0': 0.01138, 'lrf': 0.00995, 'momentum': 0.94961, 'weight_decay': 0.00048, 'warmup_epochs': 2.25751, 'warmup_momentum': 0.55648, 'box': 7.89459, 'cls': 0.3937, 'dfl': 1.88317, 'hsv_h': 0.01563, 'hsv_s': 0.6798, 'hsv_v': 0.33842, 'degrees': 0.0, 'translate': 0.09963, 'scale': 0.37552, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.47116, 'mosaic': 0.77185, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m5/30 iterations complete ✅ (1089.20s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 6/30 with hyperparameters: {'lr0': 0.01039, 'lrf': 0.00875, 'momentum': 0.94263, 'weight_decay': 0.0005, 'warmup_epochs': 2.61344, 'warmup_momentum': 0.76073, 'box': 6.66737, 'cls': 0.46531, 'dfl': 1.54382, 'hsv_h': 0.01582, 'hsv_s': 0.77458, 'hsv_v': 0.37188, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.47452, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.50866, 'mosaic': 0.91024, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m6/30 iterations complete ✅ (1303.21s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 7/30 with hyperparameters: {'lr0': 0.00959, 'lrf': 0.00939, 'momentum': 0.91165, 'weight_decay': 0.00059, 'warmup_epochs': 3.94555, 'warmup_momentum': 0.75328, 'box': 7.96153, 'cls': 0.5, 'dfl': 1.547, 'hsv_h': 0.015, 'hsv_s': 0.80113, 'hsv_v': 0.29091, 'degrees': 0.0, 'translate': 0.12876, 'scale': 0.36259, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.50006, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m7/30 iterations complete ✅ (1517.16s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 8/30 with hyperparameters: {'lr0': 0.00994, 'lrf': 0.01, 'momentum': 0.92745, 'weight_decay': 0.00051, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'box': 7.50081, 'cls': 0.49331, 'dfl': 1.46539, 'hsv_h': 0.015, 'hsv_s': 0.69298, 'hsv_v': 0.40675, 'degrees': 0.0, 'translate': 0.0979, 'scale': 0.50741, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.50847, 'mosaic': 0.97175, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m8/30 iterations complete ✅ (1730.89s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 9/30 with hyperparameters: {'lr0': 0.00994, 'lrf': 0.01009, 'momentum': 0.93363, 'weight_decay': 0.0005, 'warmup_epochs': 3.00823, 'warmup_momentum': 0.80834, 'box': 7.59048, 'cls': 0.49622, 'dfl': 1.48969, 'hsv_h': 0.01478, 'hsv_s': 0.6999, 'hsv_v': 0.4039, 'degrees': 0.0, 'translate': 0.10003, 'scale': 0.50512, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.49516, 'mosaic': 0.98754, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m9/30 iterations complete ✅ (1947.28s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 10/30 with hyperparameters: {'lr0': 0.01023, 'lrf': 0.00998, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 2.89719, 'warmup_momentum': 0.78887, 'box': 7.25204, 'cls': 0.48843, 'dfl': 1.4756, 'hsv_h': 0.01495, 'hsv_s': 0.70209, 'hsv_v': 0.40027, 'degrees': 0.0, 'translate': 0.10148, 'scale': 0.50612, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.51642, 'mosaic': 0.96635, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m10/30 iterations complete ✅ (2162.47s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 11/30 with hyperparameters: {'lr0': 0.00962, 'lrf': 0.0089, 'momentum': 0.93682, 'weight_decay': 0.0005, 'warmup_epochs': 2.61344, 'warmup_momentum': 0.85492, 'box': 6.01364, 'cls': 0.42181, 'dfl': 1.69436, 'hsv_h': 0.01586, 'hsv_s': 0.70129, 'hsv_v': 0.35169, 'degrees': 0.0, 'translate': 0.10453, 'scale': 0.47452, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.62334, 'mosaic': 0.85573, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m11/30 iterations complete ✅ (2377.28s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 12/30 with hyperparameters: {'lr0': 0.00991, 'lrf': 0.01001, 'momentum': 0.95984, 'weight_decay': 0.00058, 'warmup_epochs': 2.93082, 'warmup_momentum': 0.74406, 'box': 7.25204, 'cls': 0.47021, 'dfl': 1.38948, 'hsv_h': 0.01372, 'hsv_s': 0.62791, 'hsv_v': 0.41669, 'degrees': 0.0, 'translate': 0.10887, 'scale': 0.5018, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.52636, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m12/30 iterations complete ✅ (2590.99s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 13/30 with hyperparameters: {'lr0': 0.00987, 'lrf': 0.00959, 'momentum': 0.94052, 'weight_decay': 0.0005, 'warmup_epochs': 3.08121, 'warmup_momentum': 0.81039, 'box': 6.86561, 'cls': 0.4388, 'dfl': 1.53027, 'hsv_h': 0.01412, 'hsv_s': 0.66591, 'hsv_v': 0.38371, 'degrees': 0.0, 'translate': 0.10856, 'scale': 0.49099, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.51642, 'mosaic': 0.96635, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m13/30 iterations complete ✅ (2804.71s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 14/30 with hyperparameters: {'lr0': 0.01, 'lrf': 0.01096, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0091, 'warmup_momentum': 0.94358, 'box': 7.5, 'cls': 0.53811, 'dfl': 1.74329, 'hsv_h': 0.01594, 'hsv_s': 0.7, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.12841, 'scale': 0.42702, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m14/30 iterations complete ✅ (3016.79s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 15/30 with hyperparameters: {'lr0': 0.01065, 'lrf': 0.01, 'momentum': 0.937, 'weight_decay': 0.00053, 'warmup_epochs': 2.96351, 'warmup_momentum': 0.8209, 'box': 7.25444, 'cls': 0.52327, 'dfl': 1.5, 'hsv_h': 0.01503, 'hsv_s': 0.6988, 'hsv_v': 0.40339, 'degrees': 0.0, 'translate': 0.09713, 'scale': 0.5161, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.49608, 'mosaic': 0.95564, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m15/30 iterations complete ✅ (3229.16s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 16/30 with hyperparameters: {'lr0': 0.00985, 'lrf': 0.00936, 'momentum': 0.97046, 'weight_decay': 0.00033, 'warmup_epochs': 3.97982, 'warmup_momentum': 0.80329, 'box': 8.254, 'cls': 0.54693, 'dfl': 1.55786, 'hsv_h': 0.01318, 'hsv_s': 0.65188, 'hsv_v': 0.39107, 'degrees': 0.0, 'translate': 0.08039, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.46055, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m16/30 iterations complete ✅ (3440.65s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 17/30 with hyperparameters: {'lr0': 0.01158, 'lrf': 0.01029, 'momentum': 0.98, 'weight_decay': 0.0005, 'warmup_epochs': 4.50831, 'warmup_momentum': 0.88956, 'box': 10.12252, 'cls': 0.5, 'dfl': 1.5, 'hsv_h': 0.01502, 'hsv_s': 0.4612, 'hsv_v': 0.40179, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.48442, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 0.80813, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m17/30 iterations complete ✅ (3655.79s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 18/30 with hyperparameters: {'lr0': 0.00968, 'lrf': 0.00782, 'momentum': 0.937, 'weight_decay': 0.00053, 'warmup_epochs': 3.0, 'warmup_momentum': 0.73928, 'box': 7.29942, 'cls': 0.4658, 'dfl': 1.71942, 'hsv_h': 0.01497, 'hsv_s': 0.6824, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.08096, 'scale': 0.52377, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.50258, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m18/30 iterations complete ✅ (3869.47s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 19/30 with hyperparameters: {'lr0': 0.01093, 'lrf': 0.00975, 'momentum': 0.937, 'weight_decay': 0.00046, 'warmup_epochs': 2.25133, 'warmup_momentum': 0.58713, 'box': 5.56698, 'cls': 0.51681, 'dfl': 1.38593, 'hsv_h': 0.01703, 'hsv_s': 0.8995, 'hsv_v': 0.44767, 'degrees': 0.0, 'translate': 0.11915, 'scale': 0.51987, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.42249, 'mosaic': 0.93972, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m19/30 iterations complete ✅ (4082.32s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 20/30 with hyperparameters: {'lr0': 0.011, 'lrf': 0.01104, 'momentum': 0.97958, 'weight_decay': 0.0005, 'warmup_epochs': 1.78326, 'warmup_momentum': 0.7335, 'box': 9.68745, 'cls': 0.54528, 'dfl': 1.5, 'hsv_h': 0.01227, 'hsv_s': 0.70025, 'hsv_v': 0.51944, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.42499, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.59911, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m20/30 iterations complete ✅ (4297.23s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 21/30 with hyperparameters: {'lr0': 0.00895, 'lrf': 0.01018, 'momentum': 0.93264, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'box': 8.17712, 'cls': 0.5, 'dfl': 1.57856, 'hsv_h': 0.01576, 'hsv_s': 0.77762, 'hsv_v': 0.31866, 'degrees': 0.0, 'translate': 0.12453, 'scale': 0.39152, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.43732, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m21/30 iterations complete ✅ (4512.72s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 22/30 with hyperparameters: {'lr0': 0.00983, 'lrf': 0.01047, 'momentum': 0.94739, 'weight_decay': 0.00043, 'warmup_epochs': 3.11352, 'warmup_momentum': 0.8, 'box': 7.89517, 'cls': 0.48824, 'dfl': 1.74267, 'hsv_h': 0.01427, 'hsv_s': 0.768, 'hsv_v': 0.38111, 'degrees': 0.0, 'translate': 0.10391, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m22/30 iterations complete ✅ (4725.85s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 23/30 with hyperparameters: {'lr0': 0.00851, 'lrf': 0.00977, 'momentum': 0.93105, 'weight_decay': 0.0005, 'warmup_epochs': 3.30498, 'warmup_momentum': 0.81031, 'box': 6.72722, 'cls': 0.45154, 'dfl': 1.81091, 'hsv_h': 0.01448, 'hsv_s': 0.7, 'hsv_v': 0.37485, 'degrees': 0.0, 'translate': 0.08009, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.49255, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m23/30 iterations complete ✅ (4940.12s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 24/30 with hyperparameters: {'lr0': 0.01014, 'lrf': 0.01, 'momentum': 0.9514, 'weight_decay': 0.0005, 'warmup_epochs': 3.02899, 'warmup_momentum': 0.85148, 'box': 6.90508, 'cls': 0.52226, 'dfl': 1.61012, 'hsv_h': 0.01392, 'hsv_s': 0.73078, 'hsv_v': 0.39982, 'degrees': 0.0, 'translate': 0.09439, 'scale': 0.51815, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.50251, 'mosaic': 0.96934, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m24/30 iterations complete ✅ (5153.56s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 25/30 with hyperparameters: {'lr0': 0.00927, 'lrf': 0.00917, 'momentum': 0.93895, 'weight_decay': 0.00053, 'warmup_epochs': 3.0, 'warmup_momentum': 0.75536, 'box': 7.13256, 'cls': 0.4858, 'dfl': 1.38456, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.38776, 'degrees': 0.0, 'translate': 0.09104, 'scale': 0.48034, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.49872, 'mosaic': 0.96073, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m25/30 iterations complete ✅ (5363.44s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 26/30 with hyperparameters: {'lr0': 0.01089, 'lrf': 0.00985, 'momentum': 0.98, 'weight_decay': 0.00042, 'warmup_epochs': 2.99419, 'warmup_momentum': 0.95, 'box': 7.19423, 'cls': 0.52226, 'dfl': 1.61012, 'hsv_h': 0.01595, 'hsv_s': 0.73078, 'hsv_v': 0.39649, 'degrees': 0.0, 'translate': 0.09354, 'scale': 0.51815, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.46333, 'mosaic': 0.89668, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m26/30 iterations complete ✅ (5571.97s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 27/30 with hyperparameters: {'lr0': 0.01, 'lrf': 0.01039, 'momentum': 0.96744, 'weight_decay': 0.0005, 'warmup_epochs': 2.92527, 'warmup_momentum': 0.8, 'box': 8.00421, 'cls': 0.48922, 'dfl': 1.57423, 'hsv_h': 0.015, 'hsv_s': 0.65267, 'hsv_v': 0.38224, 'degrees': 0.0, 'translate': 0.09428, 'scale': 0.49184, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.56155, 'mosaic': 0.93618, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m27/30 iterations complete ✅ (5783.38s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 28/30 with hyperparameters: {'lr0': 0.01089, 'lrf': 0.00861, 'momentum': 0.98, 'weight_decay': 0.00042, 'warmup_epochs': 3.25588, 'warmup_momentum': 0.95, 'box': 7.278, 'cls': 0.5198, 'dfl': 1.95382, 'hsv_h': 0.01595, 'hsv_s': 0.75181, 'hsv_v': 0.39649, 'degrees': 0.0, 'translate': 0.09429, 'scale': 0.59036, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.43275, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m28/30 iterations complete ✅ (5992.85s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 29/30 with hyperparameters: {'lr0': 0.01028, 'lrf': 0.00976, 'momentum': 0.92364, 'weight_decay': 0.00045, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'box': 6.61301, 'cls': 0.47767, 'dfl': 1.59052, 'hsv_h': 0.0216, 'hsv_s': 0.75168, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.08254, 'scale': 0.57161, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.55991, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m29/30 iterations complete ✅ (6203.39s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mStarting iteration 30/30 with hyperparameters: {'lr0': 0.00611, 'lrf': 0.00985, 'momentum': 0.98, 'weight_decay': 0.00045, 'warmup_epochs': 2.12441, 'warmup_momentum': 0.87834, 'box': 6.7073, 'cls': 0.54669, 'dfl': 1.77279, 'hsv_h': 0.01167, 'hsv_s': 0.73078, 'hsv_v': 0.56567, 'degrees': 0.0, 'translate': 0.09387, 'scale': 0.51815, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.39187, 'mosaic': 0.71393, 'mixup': 0.0, 'copy_paste': 0.0}\n",
            "Saved blood_vessel_segmentation/tune/tune_scatter_plots.png\n",
            "Saved blood_vessel_segmentation/tune/tune_fitness.png\n",
            "\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0m30/30 iterations complete ✅ (6414.87s)\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mResults saved to \u001b[1mblood_vessel_segmentation/tune\u001b[0m\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness=0.34726 observed at iteration 1\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness metrics are {'metrics/precision(B)': 0.57028, 'metrics/recall(B)': 0.24676, 'metrics/mAP50(B)': 0.28238, 'metrics/mAP50-95(B)': 0.15513, 'metrics/precision(M)': 0.47646, 'metrics/recall(M)': 0.26639, 'metrics/mAP50(M)': 0.31251, 'metrics/mAP50-95(M)': 0.16461, 'val/box_loss': 1.35056, 'val/seg_loss': 1.14584, 'val/cls_loss': 1.13323, 'val/dfl_loss': 0.86719, 'fitness': 0.34726}\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness model is blood_vessel_segmentation/hyperparameter_tuning\n",
            "\u001b[34m\u001b[1mTuner: \u001b[0mBest fitness hyperparameters are printed below.\n",
            "\n",
            "Printing '\u001b[1m\u001b[30mblood_vessel_segmentation/tune/best_hyperparameters.yaml\u001b[0m'\n",
            "\n",
            "lr0: 0.01\n",
            "lrf: 0.01\n",
            "momentum: 0.937\n",
            "weight_decay: 0.0005\n",
            "warmup_epochs: 3.0\n",
            "warmup_momentum: 0.8\n",
            "box: 7.5\n",
            "cls: 0.5\n",
            "dfl: 1.5\n",
            "hsv_h: 0.015\n",
            "hsv_s: 0.7\n",
            "hsv_v: 0.4\n",
            "degrees: 0.0\n",
            "translate: 0.1\n",
            "scale: 0.5\n",
            "shear: 0.0\n",
            "perspective: 0.0\n",
            "flipud: 0.0\n",
            "fliplr: 0.5\n",
            "mosaic: 1.0\n",
            "mixup: 0.0\n",
            "copy_paste: 0.0\n",
            "\n"
          ]
        }
      ],
      "source": [
        "model.tune(data=yaml_path,\n",
        "            project=project,\n",
        "            name=name,\n",
        "            epochs=10,\n",
        "            imgsz=1024,\n",
        "            iterations=30,\n",
        "            save=False,\n",
        "            cache=True,\n",
        "            batch=4,\n",
        "            )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "73s4adZKjTDi"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPlD9PqRNDciLkAxsfwa+F7",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}