val.ipynb · 渤海之上皓月当空/YOLOv8

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{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "【导包】"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ee343d41d3ebbcb1"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-09-19T14:15:50.893439Z",
     "start_time": "2023-09-19T14:15:50.870398300Z"
    }
   },
   "outputs": [],
   "source": [
    "from ultralytics import YOLO"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "【设置所需文件路径】"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1c603eb143c0b487"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "# model_path = 'runs/detect/train/weights/best.pt'  # 模型路径\n",
    "model_path = 'runs/detect/train2/weights/best.pt'  # 模型路径"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-19T14:15:50.893439Z",
     "start_time": "2023-09-19T14:15:50.875932900Z"
    }
   },
   "id": "9e1f095819a9e4ab"
  },
  {
   "cell_type": "markdown",
   "source": [
    "【加载模型】"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "79328f76066b2edf"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "model = YOLO(model_path)  # load a pretrained model (recommended for training)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-19T14:15:50.931995600Z",
     "start_time": "2023-09-19T14:15:50.878439600Z"
    }
   },
   "id": "2aa7668ca46a4113"
  },
  {
   "cell_type": "markdown",
   "source": [
    "【模型验证】"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "925746baba17a02d"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.180  Python-3.11.5 torch-2.0.1 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)\n",
      "Model summary (fused): 168 layers, 3007793 parameters, 0 gradients\n",
      "\u001B[34m\u001B[1mval: \u001B[0mScanning D:\\JetBrains\\PyCharm 2023.2\\PycharmProjects\\YOLOv8\\datasets\\game\\labels\\val.cache... 64 images, 0 backgrounds, 0 corrupt: 100%|██████████| 64/64 [00:00<?, ?it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:04<00:00,  1.22s/it]\n",
      "                   all         64       1414      0.923      0.819      0.875      0.698\n",
      "                person         64         45      0.971      0.752      0.851      0.626\n",
      "                 grass         64         77      0.967      0.961      0.984       0.81\n",
      "                flower         64        149      0.907      0.852      0.904      0.676\n",
      "                  rock         64        102       0.98      0.481      0.612      0.413\n",
      "                  tree         64        557      0.926      0.874      0.953      0.735\n",
      "                bridge         64         23      0.952       0.87      0.988      0.803\n",
      "              mountain         64        106      0.975          1      0.995      0.926\n",
      "              building         64        109      0.949      0.945      0.979      0.855\n",
      "                  bush         64        233      0.887      0.676      0.788      0.515\n",
      "                  idol         64          5      0.858        0.6        0.6      0.559\n",
      "                  boat         64          8      0.779          1      0.971       0.76\n",
      "Speed: 0.8ms preprocess, 1.6ms inference, 0.0ms loss, 7.6ms postprocess per image\n",
      "Results saved to \u001B[1mruns\\detect\\val2\u001B[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型评估参数表：\n",
      "[    0.62626     0.81031      0.6759     0.41334     0.73485     0.80322     0.92634     0.85486      0.5155     0.55943     0.76002]\n"
     ]
    }
   ],
   "source": [
    "metrics = model.val()\n",
    "print(\"模型评估参数表：\")\n",
    "print(metrics.box.maps)  # a list contains map50-95 of each category"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-19T14:15:57.730582800Z",
     "start_time": "2023-09-19T14:15:50.919996200Z"
    }
   },
   "id": "3510efbdf3445a81"
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}

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提交于 2023-09-20 14:47 . 验证模型

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