Vigyaan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6f86af6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torch\n",
      "  Obtaining dependency information for torch from https://files.pythonhosted.org/packages/7b/7c/4d8728e6f8dbe2b8af054bd92c290d94c633270443514e3ee4b768125cf9/torch-2.1.0-cp311-none-macosx_11_0_arm64.whl.metadata\n",
      "  Downloading torch-2.1.0-cp311-none-macosx_11_0_arm64.whl.metadata (24 kB)\n",
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      "Downloading torch-2.1.0-cp311-none-macosx_11_0_arm64.whl (59.6 MB)\n",
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      "\u001b[?25hInstalling collected packages: torch\n",
      "Successfully installed torch-2.1.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!pip install torch  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c9ec14",
   "metadata": {},
   "source": [
    "# Installing Yolov5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c400f2de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'yolov5'...\n",
      "remote: Enumerating objects: 16003, done.\u001b[K\n",
      "remote: Counting objects: 100% (36/36), done.\u001b[K\n",
      "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
      "remote: Total 16003 (delta 21), reused 20 (delta 13), pack-reused 15967\u001b[K\n",
      "Receiving objects: 100% (16003/16003), 14.60 MiB | 10.13 MiB/s, done.\n",
      "Resolving deltas: 100% (10987/10987), done.\n",
      "/Users/pranayagrawal/Desktop/yolov5\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from IPython.display import Image \n",
    "!git clone https://github.com/ultralytics/yolov5\n",
    "%cd yolov5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e99ff033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gitpython>=3.1.30 (from -r requirements.txt (line 5))\n",
      "  Obtaining dependency information for gitpython>=3.1.30 from https://files.pythonhosted.org/packages/8a/7e/20f7e45878b5aed34320fbeeae8f78acc806e7bd708d00b1c6e64b016f5b/GitPython-3.1.37-py3-none-any.whl.metadata\n",
      "  Downloading GitPython-3.1.37-py3-none-any.whl.metadata (12 kB)\n",
      "Requirement already satisfied: matplotlib>=3.3 in /Users/pranayagrawal/anaconda3/lib/python3.11/site-packages (from -r requirements.txt (line 6)) (3.7.2)\n",
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      "Collecting opencv-python>=4.1.1 (from -r requirements.txt (line 8))\n",
      "  Obtaining dependency information for opencv-python>=4.1.1 from https://files.pythonhosted.org/packages/a1/f6/57de91ea40c670527cd47a6548bf2cbedc68cec57c041793b256356abad7/opencv_python-4.8.1.78-cp37-abi3-macosx_11_0_arm64.whl.metadata\n",
      "  Downloading opencv_python-4.8.1.78-cp37-abi3-macosx_11_0_arm64.whl.metadata (19 kB)\n",
      "Requirement already satisfied: Pillow>=7.1.2 in /Users/pranayagrawal/anaconda3/lib/python3.11/site-packages (from -r requirements.txt (line 9)) (9.4.0)\n",
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      "Collecting thop>=0.1.1 (from -r requirements.txt (line 14))\n",
      "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
      "Requirement already satisfied: torch>=1.8.0 in /Users/pranayagrawal/anaconda3/lib/python3.11/site-packages (from -r requirements.txt (line 15)) (2.1.0)\n",
      "Collecting torchvision>=0.9.0 (from -r requirements.txt (line 16))\n",
      "  Obtaining dependency information for torchvision>=0.9.0 from https://files.pythonhosted.org/packages/dd/48/85ef87f1548620d7c3743cbe135b90d8cf673fc7b8594361917558d765b6/torchvision-0.16.0-cp311-cp311-macosx_11_0_arm64.whl.metadata\n",
      "  Downloading torchvision-0.16.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (6.6 kB)\n",
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      "Collecting ultralytics>=8.0.147 (from -r requirements.txt (line 18))\n",
      "  Obtaining dependency information for ultralytics>=8.0.147 from https://files.pythonhosted.org/packages/76/bc/f5dc5cfd3625dde7d873cbe537f9dc2c04f7329fdf238347140ac42f4634/ultralytics-8.0.195-py3-none-any.whl.metadata\n",
      "  Downloading ultralytics-8.0.195-py3-none-any.whl.metadata (31 kB)\n",
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      "Collecting gitdb<5,>=4.0.1 (from gitpython>=3.1.30->-r requirements.txt (line 5))\n",
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      "Requirement already satisfied: filelock in /Users/pranayagrawal/anaconda3/lib/python3.11/site-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (3.9.0)\n",
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      "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython>=3.1.30->-r requirements.txt (line 5))\n",
      "  Obtaining dependency information for smmap<6,>=3.0.1 from https://files.pythonhosted.org/packages/a7/a5/10f97f73544edcdef54409f1d839f6049a0d79df68adbc1ceb24d1aaca42/smmap-5.0.1-py3-none-any.whl.metadata\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hDownloading torchvision-0.16.0-cp311-cp311-macosx_11_0_arm64.whl (1.6 MB)\n",
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      "\u001b[?25hDownloading ultralytics-8.0.195-py3-none-any.whl (618 kB)\n",
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      "\u001b[?25hDownloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
      "Installing collected packages: smmap, opencv-python, gitdb, torchvision, thop, gitpython, ultralytics\n",
      "Successfully installed gitdb-4.0.10 gitpython-3.1.37 opencv-python-4.8.1.78 smmap-5.0.1 thop-0.1.1.post2209072238 torchvision-0.16.0 ultralytics-8.0.195\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68bea85d",
   "metadata": {},
   "source": [
    "# Installing the dataset to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0664ffd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting roboflow\n",
      "  Obtaining dependency information for roboflow from https://files.pythonhosted.org/packages/4f/2c/13ef4a1811365208f7441dae564ea57dad4b58762196b7d8e65860b0cc07/roboflow-1.1.7-py3-none-any.whl.metadata\n",
      "  Downloading roboflow-1.1.7-py3-none-any.whl.metadata (9.0 kB)\n",
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      "  Obtaining dependency information for opencv-python-headless==4.8.0.74 from https://files.pythonhosted.org/packages/9d/df/88bc1f767d880d4629d71d6d3907bf89aea4506b828e9ede7d3846a0912d/opencv_python_headless-4.8.0.74-cp37-abi3-macosx_11_0_arm64.whl.metadata\n",
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      "  Obtaining dependency information for supervision from https://files.pythonhosted.org/packages/51/81/3314df2b9f84c0101ba3980499f2b3d87632e19ceaaeaa922fa7c697fa2c/supervision-0.15.0-py3-none-any.whl.metadata\n",
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      "Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /Users/pranayagrawal/anaconda3/lib/python3.11/site-packages (from supervision->roboflow) (1.11.1)\n",
      "Downloading roboflow-1.1.7-py3-none-any.whl (58 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading opencv_python_headless-4.8.0.74-cp37-abi3-macosx_11_0_arm64.whl (33.1 MB)\n",
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      "\u001b[?25hDownloading supervision-0.15.0-py3-none-any.whl (69 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.0/69.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pyparsing, opencv-python-headless, idna, cycler, certifi, supervision, roboflow\n",
      "  Attempting uninstall: pyparsing\n",
      "    Found existing installation: pyparsing 3.0.9\n",
      "    Uninstalling pyparsing-3.0.9:\n",
      "      Successfully uninstalled pyparsing-3.0.9\n",
      "  Attempting uninstall: idna\n",
      "    Found existing installation: idna 3.4\n",
      "    Uninstalling idna-3.4:\n",
      "      Successfully uninstalled idna-3.4\n",
      "  Attempting uninstall: cycler\n",
      "    Found existing installation: cycler 0.11.0\n",
      "    Uninstalling cycler-0.11.0:\n",
      "      Successfully uninstalled cycler-0.11.0\n",
      "  Attempting uninstall: certifi\n",
      "    Found existing installation: certifi 2023.7.22\n",
      "    Uninstalling certifi-2023.7.22:\n",
      "      Successfully uninstalled certifi-2023.7.22\n",
      "Successfully installed certifi-2022.12.7 cycler-0.10.0 idna-2.10 opencv-python-headless-4.8.0.74 pyparsing-2.4.7 roboflow-1.1.7 supervision-0.15.0\n",
      "loading Roboflow workspace...\n",
      "loading Roboflow project...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading Dataset Version Zip in Defect-2 to yolov5pytorch:: 100%|█| 94137/941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Extracting Dataset Version Zip to Defect-2 in yolov5pytorch:: 100%|█| 4920/4920 \n"
     ]
    }
   ],
   "source": [
    "!pip install roboflow\n",
    "\n",
    "from roboflow import Roboflow\n",
    "rf = Roboflow(api_key=\"k8Eyo3oHMDidqFG838V2\")\n",
    "project = rf.workspace(\"vigyaan\").project(\"defect-irxl0\")\n",
    "dataset = project.version(2).download(\"yolov5\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01320d08",
   "metadata": {},
   "source": [
    "# Training the YOLO model on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a897c78a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/Users/pranayagrawal/Desktop/yolov5/Defect-2/data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=30, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
      "YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.11.5 torch-2.1.0 CPU\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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",
      "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/\n",
      "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
      "100%|██████████████████████████████████████| 14.1M/14.1M [00:02<00:00, 5.40MB/s]\n",
      "\n",
      "Overriding model.yaml nc=80 with nc=6\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     29667  models.yolo.Detect                      [6, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model summary: 214 layers, 7035811 parameters, 7035811 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 343/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /Users/pranayagrawal/Desktop/yolov5/Defect-2/train/labels.cache.\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /Users/pranayagrawal/Desktop/yolov5/Defect-2/train/images/inclusion_62_jpg.rf.0f111158685b11acb63e87b45258ac4f.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /Users/pranayagrawal/Desktop/yolov5/Defect-2/train/images/inclusion_62_jpg.rf.9ab0659fa80a2ae62d9c11e5c6a2a057.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /Users/pranayagrawal/Desktop/yolov5/Defect-2/train/images/patches_198_jpg.rf.113afaf2ef7c4c7f152e7dc2795fdd21.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /Users/pranayagrawal/Desktop/yolov5/Defect-2/train/images/patches_198_jpg.rf.18a980a03d3ec3072f85fef61e169d98.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /Users/pranayagrawal/Desktop/yolov5/Defect-2/valid/labels.cache...\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /Users/pranayagrawal/Desktop/yolov5/Defect-2/valid/images/crazing_120_jpg.rf.0d0882cc0f1bf6f1c12207e1e6359afc.jpg: 1 duplicate labels removed\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.21 anchors/target, 0.987 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to yolov5/runs/train/exp2/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1myolov5/runs/train/exp2\u001b[0m\n",
      "Starting training for 30 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/29         0G    0.08872    0.03973     0.0486         33        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825     0.0767      0.163     0.0399     0.0131\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/29         0G    0.06067    0.03918    0.02602         49        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825       0.39       0.38      0.253     0.0965\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/29         0G    0.05641    0.03527     0.0105         47        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.355      0.415       0.35      0.134\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/29         0G    0.05379    0.03374   0.008683         52        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.424       0.19      0.185     0.0712\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/29         0G    0.05084     0.0348   0.006845         63        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.532      0.369      0.363      0.151\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/29         0G    0.04847    0.03371   0.005833         51        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.374      0.299      0.319      0.135\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/29         0G    0.04586    0.03439   0.004958         51        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.311       0.22      0.197     0.0664\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/29         0G    0.04441    0.03367   0.004432         55        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.554      0.605      0.614      0.291\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/29         0G    0.04252    0.03323   0.004301         63        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.555      0.441       0.48      0.201\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/29         0G    0.04201    0.03318   0.003815         49        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.535      0.524      0.551      0.254\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/29         0G    0.04093    0.03192   0.003618         44        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.564      0.629      0.631      0.291\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/29         0G    0.04017    0.03304   0.002821         56        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825       0.67      0.592      0.664      0.318\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/29         0G    0.03934    0.03218   0.003014         52        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825       0.42      0.383      0.401      0.174\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/29         0G    0.03916    0.03134   0.002663         54        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.644      0.661      0.707      0.344\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/29         0G    0.03811    0.03097   0.002629         48        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.694      0.634      0.685      0.336\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/29         0G    0.03722    0.03215   0.002197         63        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825       0.61      0.664      0.677      0.345\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/29         0G    0.03709    0.03141   0.002182         45        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.657      0.653      0.703      0.361\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/29         0G    0.03646    0.03149   0.002303         66        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.615      0.687       0.69       0.34\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/29         0G     0.0363    0.03112   0.002483         44        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.668      0.657      0.704       0.34\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/29         0G    0.03602     0.0309    0.00199         34        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.628      0.681      0.699      0.348\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/29         0G    0.03486    0.03092   0.001829         48        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.653      0.663      0.683      0.347\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/29         0G    0.03449    0.03047   0.001639         50        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.682       0.69      0.715      0.352\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/29         0G    0.03409    0.03061   0.001553         66        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.631      0.724      0.713      0.361\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/29         0G    0.03432    0.02988   0.001673         46        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.667      0.677      0.718      0.363\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/29         0G     0.0334     0.0303   0.001387         39        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.693      0.708       0.75      0.385\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/29         0G    0.03256    0.02999   0.001433         48        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.675      0.699      0.728      0.375\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/29         0G    0.03256    0.02933   0.001738         49        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.681      0.689      0.728      0.384\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/29         0G    0.03212    0.02982   0.001553         38        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.715      0.663      0.733      0.377\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/29         0G    0.03138    0.02912   0.001156         41        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.681      0.697      0.737      0.381\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/29         0G    0.03164    0.02935   0.001162         59        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825       0.65       0.72      0.734      0.388\n",
      "\n",
      "30 epochs completed in 15.603 hours.\n",
      "Optimizer stripped from yolov5/runs/train/exp2/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from yolov5/runs/train/exp2/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating yolov5/runs/train/exp2/weights/best.pt...\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7026307 parameters, 0 gradients, 15.8 GFLOPs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        360        825      0.653      0.719      0.734      0.389\n",
      "               crazing        360        139      0.429      0.252      0.327      0.127\n",
      "             inclusion        360        215      0.626       0.86      0.837      0.416\n",
      "               patches        360        155      0.746      0.916      0.927       0.57\n",
      "        pitted_surface        360         88      0.861       0.75      0.813      0.459\n",
      "       rolled-in_scale        360        129      0.552      0.636      0.616      0.308\n",
      "             scratches        360         99      0.704      0.899      0.881      0.453\n",
      "Results saved to \u001b[1myolov5/runs/train/exp2\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!python /Users/pranayagrawal/Desktop/yolov5/train.py --img 640 --epochs 30 --data /Users/pranayagrawal/Desktop/yolov5/Defect-2/data.yaml --weights yolov5s.pt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8298334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.pt'], source=/Users/pranayagrawal/Desktop/yolov5/Defect-2/test/images/crazing_14_jpg.rf.d4f3493b97ac826908accd65f2d526a9.jpg, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.11.5 torch-2.1.0 CPU\n",
      "\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7026307 parameters, 0 gradients, 15.8 GFLOPs\n",
      "image 1/1 /Users/pranayagrawal/Desktop/yolov5/Defect-2/test/images/crazing_14_jpg.rf.d4f3493b97ac826908accd65f2d526a9.jpg: 640x640 (no detections), 150.1ms\n",
      "Speed: 0.7ms pre-process, 150.1ms inference, 3.7ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1myolov5/runs/detect/exp2\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!python /Users/pranayagrawal/Desktop/yolov5/detect.py --source /Users/pranayagrawal/Desktop/yolov5/Defect-2/test/images/crazing_14_jpg.rf.d4f3493b97ac826908accd65f2d526a9.jpg --weights /Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.pt\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7309a9b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.pt'], source=/Users/pranayagrawal/Desktop/yolov5/Defect-2/test/images/patches_239_jpg.rf.4b11892e3c0a2259d21a224e54bad7ba.jpg, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.11.5 torch-2.1.0 CPU\n",
      "\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7026307 parameters, 0 gradients, 15.8 GFLOPs\n",
      "image 1/1 /Users/pranayagrawal/Desktop/yolov5/Defect-2/test/images/patches_239_jpg.rf.4b11892e3c0a2259d21a224e54bad7ba.jpg: 640x640 2 patchess, 129.0ms\n",
      "Speed: 0.9ms pre-process, 129.0ms inference, 5.3ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1myolov5/runs/detect/exp10\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!python /Users/pranayagrawal/Desktop/yolov5/detect.py --source /Users/pranayagrawal/Desktop/yolov5/Defect-2/test/images/patches_239_jpg.rf.4b11892e3c0a2259d21a224e54bad7ba.jpg --weights /Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.pt\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25768cbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mexport: \u001b[0mdata=yolov5/data/coco128.yaml, weights=['/Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['onnx']\n",
      "YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.11.5 torch-2.1.0 CPU\n",
      "\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7026307 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from /Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.pt with output shape (1, 25200, 11) (13.8 MB)\n",
      "\n",
      "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.14.1...\n",
      "\u001b[34m\u001b[1mONNX:\u001b[0m simplifying with onnx-simplifier 0.4.33...\n",
      "\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 2.4s, saved as /Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.onnx (27.2 MB)\n",
      "\n",
      "Export complete (2.8s)\n",
      "Results saved to \u001b[1m/Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights\u001b[0m\n",
      "Detect:          python detect.py --weights /Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.onnx \n",
      "Validate:        python val.py --weights /Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.onnx \n",
      "PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '/Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.onnx')  \n",
      "Visualize:       https://netron.app\n"
     ]
    }
   ],
   "source": [
    "!python /Users/pranayagrawal/Desktop/yolov5/export.py --weights /Users/pranayagrawal/Desktop/yolov5/runs/train/exp2/weights/best.pt --include onnx --simplify --opset 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d691589c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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