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Added model files and notebook

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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+superresolution.jpg filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/notebookcf133ce21c-checkpoint.ipynb

@@ -0,0 +1,814 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sat Mar  4 21:14:41 2023       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 531.18                 Driver Version: 531.18       CUDA Version: 12.1     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                      TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  NVIDIA GeForce GTX 1080 Ti    WDDM | 00000000:B3:00.0  On |                  N/A |\n",
+      "|100%   42C    P0               68W / 127W|   1980MiB / 11264MiB |      3%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|    0   N/A  N/A      1148    C+G   ... (x86)\\Audeze\\AudezeHQ\\AudezeHQ.exe    N/A      |\n",
+      "|    0   N/A  N/A      2232    C+G   ...GeForce Experience\\NVIDIA Share.exe    N/A      |\n",
+      "|    0   N/A  N/A      6208    C+G   ..._x64__rz1tebttyb220\\DolbyAccess.exe    N/A      |\n",
+      "|    0   N/A  N/A      9096    C+G   ...CBS_cw5n1h2txyewy\\TextInputHost.exe    N/A      |\n",
+      "|    0   N/A  N/A      9832    C+G   ...63.0_x86__zpdnekdrzrea0\\Spotify.exe    N/A      |\n",
+      "|    0   N/A  N/A     10856    C+G   ....0_x64__8wekyb3d8bbwe\\HxOutlook.exe    N/A      |\n",
+      "|    0   N/A  N/A     11512    C+G   ...2txyewy\\StartMenuExperienceHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     11596    C+G   ...GeForce Experience\\NVIDIA Share.exe    N/A      |\n",
+      "|    0   N/A  N/A     12780    C+G   ...rPicker\\PowerToys.ColorPickerUI.exe    N/A      |\n",
+      "|    0   N/A  N/A     14676    C+G   ...on\\110.0.1587.57\\msedgewebview2.exe    N/A      |\n",
+      "|    0   N/A  N/A     14900    C+G   C:\\Windows\\explorer.exe                   N/A      |\n",
+      "|    0   N/A  N/A     16116    C+G   ...FancyZones\\PowerToys.FancyZones.exe    N/A      |\n",
+      "|    0   N/A  N/A     17548    C+G   ...5n1h2txyewy\\ShellExperienceHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     17848      C   ...al\\Discord\\app-1.0.9011\\Discord.exe    N/A      |\n",
+      "|    0   N/A  N/A     18064    C+G   ...\\cef\\cef.win7x64\\steamwebhelper.exe    N/A      |\n",
+      "|    0   N/A  N/A     20132    C+G   ...crosoft\\Edge\\Application\\msedge.exe    N/A      |\n",
+      "|    0   N/A  N/A     20864    C+G   ...t.LockApp_cw5n1h2txyewy\\LockApp.exe    N/A      |\n",
+      "|    0   N/A  N/A     20928    C+G   ...B\\system_tray\\lghub_system_tray.exe    N/A      |\n",
+      "|    0   N/A  N/A     22896    C+G   ...Cloudflare WARP\\Cloudflare WARP.exe    N/A      |\n",
+      "|    0   N/A  N/A     23784    C+G   ...__8wekyb3d8bbwe\\WindowsTerminal.exe    N/A      |\n",
+      "|    0   N/A  N/A     24416    C+G   ...cal\\Microsoft\\OneDrive\\OneDrive.exe    N/A      |\n",
+      "|    0   N/A  N/A     27532    C+G   ...auncher\\PowerToys.PowerLauncher.exe    N/A      |\n",
+      "|    0   N/A  N/A     28696    C+G   ...siveControlPanel\\SystemSettings.exe    N/A      |\n",
+      "|    0   N/A  N/A     29184    C+G   ...ekyb3d8bbwe\\PhoneExperienceHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     32684    C+G   ...nt.CBS_cw5n1h2txyewy\\SearchHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     34624    C+G   C:\\Program Files\\LGHUB\\lghub.exe          N/A      |\n",
+      "|    0   N/A  N/A     34692    C+G   ...pdnekdrzrea0\\XboxGameBarSpotify.exe    N/A      |\n",
+      "|    0   N/A  N/A     37692    C+G   ...4.0_x64__cv1g1gvanyjgm\\WhatsApp.exe    N/A      |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "HOME = os.getcwd()\n",
+    "print(HOME)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Gyana\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython import display\n",
+    "display.clear_output()\n",
+    "\n",
+    "from ultralytics import YOLOV8\n",
+    "ultralytics.checks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install roboflow\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'roboflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mroboflow\u001b[39;00m \u001b[39mimport\u001b[39;00m Roboflow\n\u001b[0;32m      2\u001b[0m rf \u001b[39m=\u001b[39m Roboflow(api_key\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m2NdQm1ivtFCAYiOLVTwn\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m project \u001b[39m=\u001b[39m rf\u001b[39m.\u001b[39mworkspace(\u001b[39m\"\u001b[39m\u001b[39mhackthethong\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mproject(\u001b[39m\"\u001b[39m\u001b[39mpothole-detection-gmnid\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'roboflow'"
+     ]
+    }
+   ],
+   "source": [
+    "from roboflow import Roboflow\n",
+    "rf = Roboflow(api_key=\"2NdQm1ivtFCAYiOLVTwn\")\n",
+    "project = rf.workspace(\"hackthethong\").project(\"pothole-detection-gmnid\")\n",
+    "dataset = project.version(3).download(\"yolov8\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:34.982368Z",
+     "iopub.status.busy": "2023-03-04T11:06:34.982065Z",
+     "iopub.status.idle": "2023-03-04T11:06:36.155978Z",
+     "shell.execute_reply": "2023-03-04T11:06:36.154454Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:34.982341Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sat Mar  4 11:06:35 2023       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 470.82.01    Driver Version: 470.82.01    CUDA Version: 11.4     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   36C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   1  Tesla T4            Off  | 00000000:00:05.0 Off |                    0 |\n",
+      "| N/A   47C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:36.165528Z",
+     "iopub.status.busy": "2023-03-04T11:06:36.162799Z",
+     "iopub.status.idle": "2023-03-04T11:06:36.175759Z",
+     "shell.execute_reply": "2023-03-04T11:06:36.174308Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:36.165476Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/working\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "HOME = os.getcwd()\n",
+    "print(HOME)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:36.180607Z",
+     "iopub.status.busy": "2023-03-04T11:06:36.179797Z",
+     "iopub.status.idle": "2023-03-04T11:06:55.605740Z",
+     "shell.execute_reply": "2023-03-04T11:06:55.604691Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:36.180564Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ultralytics YOLOv8.0.20 🚀 Python-3.7.12 torch-1.13.0 CUDA:0 (Tesla T4, 15110MiB)\n",
+      "Setup complete ✅ (2 CPUs, 15.6 GB RAM, 4437.1/8062.4 GB disk)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pip install method (recommended)\n",
+    "\n",
+    "!pip install ultralytics==8.0.20\n",
+    "\n",
+    "from IPython import display\n",
+    "display.clear_output()\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:55.608863Z",
+     "iopub.status.busy": "2023-03-04T11:06:55.608358Z",
+     "iopub.status.idle": "2023-03-04T11:06:55.615639Z",
+     "shell.execute_reply": "2023-03-04T11:06:55.613293Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:55.608820Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ultralytics import YOLO\n",
+    "\n",
+    "from IPython.display import display, Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:55.618151Z",
+     "iopub.status.busy": "2023-03-04T11:06:55.617309Z",
+     "iopub.status.idle": "2023-03-04T11:07:53.787811Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/working/datasets\n",
+      "Collecting roboflow\n",
+      "  Downloading roboflow-0.2.32-py3-none-any.whl (50 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: opencv-python>=4.1.2 in /opt/conda/lib/python3.7/site-packages (from roboflow) (4.7.0.72)\n",
+      "Collecting wget\n",
+      "  Downloading wget-3.2.zip (10 kB)\n",
+      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25hRequirement already satisfied: urllib3>=1.26.6 in /opt/conda/lib/python3.7/site-packages (from roboflow) (1.26.14)\n",
+      "Requirement already satisfied: python-dotenv in /opt/conda/lib/python3.7/site-packages (from roboflow) (0.21.1)\n",
+      "Requirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.7/site-packages (from roboflow) (9.4.0)\n",
+      "Collecting chardet==4.0.0\n",
+      "  Downloading chardet-4.0.0-py2.py3-none-any.whl (178 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: tqdm>=4.41.0 in /opt/conda/lib/python3.7/site-packages (from roboflow) (4.64.1)\n",
+      "Collecting idna==2.10\n",
+      "  Downloading idna-2.10-py2.py3-none-any.whl (58 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from roboflow) (3.5.3)\n",
+      "Collecting cycler==0.10.0\n",
+      "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
+      "Collecting requests-toolbelt\n",
+      "  Downloading requests_toolbelt-0.10.1-py2.py3-none-any.whl (54 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: python-dateutil in /opt/conda/lib/python3.7/site-packages (from roboflow) (2.8.2)\n",
+      "Requirement already satisfied: certifi==2022.12.7 in /opt/conda/lib/python3.7/site-packages (from roboflow) (2022.12.7)\n",
+      "Requirement already satisfied: numpy>=1.18.5 in /opt/conda/lib/python3.7/site-packages (from roboflow) (1.21.6)\n",
+      "Requirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.7/site-packages (from roboflow) (6.0)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from roboflow) (2.28.2)\n",
+      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from roboflow) (1.16.0)\n",
+      "Collecting pyparsing==2.4.7\n",
+      "  Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.7/site-packages (from roboflow) (1.4.4)\n",
+      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.3.1->roboflow) (4.4.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->roboflow) (4.38.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->roboflow) (23.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->roboflow) (2.1.1)\n",
+      "Building wheels for collected packages: wget\n",
+      "  Building wheel for wget (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9674 sha256=c21353920025ab2a7acd6c41f94a830e3de43131a8eb128b751e9613559978c7\n",
+      "  Stored in directory: /root/.cache/pip/wheels/e1/e8/db/ebe4dcd7d7d11208c1e4e4ef246cea4fcc8d463c93405a6555\n",
+      "Successfully built wget\n",
+      "Installing collected packages: wget, pyparsing, idna, cycler, chardet, requests-toolbelt, 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",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "librosa 0.10.0 requires soundfile>=0.12.1, but you have soundfile 0.11.0 which is incompatible.\n",
+      "cloud-tpu-client 0.10 requires google-api-python-client==1.8.0, but you have google-api-python-client 2.79.0 which is incompatible.\n",
+      "apache-beam 2.44.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.6 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0mSuccessfully installed chardet-4.0.0 cycler-0.10.0 idna-2.10 pyparsing-2.4.7 requests-toolbelt-0.10.1 roboflow-0.2.32 wget-3.2\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "loading Roboflow workspace...\n",
+      "loading Roboflow project...\n",
+      "Downloading Dataset Version Zip in Pothole-detection-1 to yolov8: 97% [254164992 / 260273386] bytes"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir {HOME}/datasets\n",
+    "%cd {HOME}/datasets\n",
+    "\n",
+    "!pip install roboflow\n",
+    "\n",
+    "from roboflow import Roboflow\n",
+    "rf = Roboflow(api_key=\"2NdQm1ivtFCAYiOLVTwn\")\n",
+    "project = rf.workspace(\"hackthethong\").project(\"pothole-detection-gmnid\")\n",
+    "dataset = project.version(1).download(\"yolov8\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:16:50.287812Z",
+     "iopub.status.busy": "2023-03-04T11:16:50.287079Z",
+     "iopub.status.idle": "2023-03-04T12:12:27.477858Z",
+     "shell.execute_reply": "2023-03-04T12:12:27.476221Z",
+     "shell.execute_reply.started": "2023-03-04T11:16:50.287774Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/working\n",
+      "Ultralytics YOLOv8.0.20 🚀 Python-3.7.12 torch-1.13.0 CUDA:0 (Tesla T4, 15110MiB)\n",
+      "                                                      CUDA:1 (Tesla T4, 15110MiB)\n",
+      "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.yaml, data=/kaggle/working/datasets/Pothole-detection-1/data.yaml, epochs=215, patience=50, batch=24, imgsz=800, save=True, cache=False, device=(0, 1), workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=ultralytics/assets/, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.001, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs/detect/train3\n",
+      "Overriding model.yaml nc=80 with nc=1\n",
+      "\n",
+      "                   from  n    params  module                                       arguments                     \n",
+      "  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 \n",
+      "  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
+      "  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             \n",
+      "  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
+      "  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
+      "  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
+      "  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           \n",
+      "  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              \n",
+      "  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           \n",
+      "  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 \n",
+      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 \n",
+      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
+      " 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
+      " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
+      " 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              \n",
+      " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 \n",
+      " 22        [15, 18, 21]  1   2116435  ultralytics.nn.modules.Detect                [1, [128, 256, 512]]          \n",
+      "Model summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs\n",
+      "\n",
+      "Transferred 349/355 items from pretrained weights\n",
+      "DDP settings: RANK 0, WORLD_SIZE 2, DEVICE cuda:0\n",
+      "Overriding model.yaml nc=80 with nc=1\n",
+      "\n",
+      "                   from  n    params  module                                       arguments                     \n",
+      "  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 \n",
+      "  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
+      "  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             \n",
+      "  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
+      "  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
+      "  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
+      "  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           \n",
+      "  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              \n",
+      "  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           \n",
+      "  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 \n",
+      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 \n",
+      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
+      " 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
+      " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
+      " 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              \n",
+      " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 \n",
+      " 22        [15, 18, 21]  1   2116435  ultralytics.nn.modules.Detect                [1, [128, 256, 512]]          \n",
+      "YOLOv8s summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs\n",
+      "\n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0011250000000000001), 63 bias\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/datasets/Pothole-detection-1/train/labels.cache.\u001b[0m\n",
+      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/datasets/Pothole-detection-1/valid/labels.cache...\u001b[0m\n",
+      "Image sizes 800 train, 800 val\n",
+      "Using 2 dataloader workers\n",
+      "Logging results to \u001b[1mruns/detect/train3\u001b[0m\n",
+      "Starting training for 215 epochs...\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      1/215      5.86G      3.481      4.528       4.09          7        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941   0.000902     0.0967   0.000495   0.000148\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      2/215      6.96G      3.266      3.481      3.476          2        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941   0.000937     0.0786   0.000536   0.000168\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      3/215      6.96G       2.43      2.349      2.433          7        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00105    0.00213    0.00035   0.000101\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      4/215      6.96G      1.867       1.71       1.77          8        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00488     0.0117     0.0025    0.00104\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      5/215      6.96G      1.549      1.331       1.48          3        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941      0.023     0.0266    0.00589     0.0015\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      6/215      6.96G      1.341      1.113       1.34         17        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00154    0.00531   0.000551   0.000166\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      7/215      6.96G      1.187     0.9461      1.234         14        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00854     0.0298    0.00488    0.00147\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      8/215      6.96G       1.08     0.8943      1.175          8        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0174     0.0765     0.0116    0.00329\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      9/215      6.96G      1.012     0.8116      1.133          5        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0058     0.0085    0.00295    0.00103\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     10/215      6.96G     0.9529      0.756      1.114          6        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0551     0.0351     0.0156    0.00493\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     11/215      6.96G     0.8946     0.7085      1.069          3        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00808      0.051    0.00986    0.00449\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     12/215      6.96G     0.8897     0.7045      1.057          4        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0929     0.0244     0.0159    0.00461\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     13/215      6.96G     0.8399     0.6641      1.048          5        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0321     0.0893     0.0204    0.00543\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     14/215      6.96G     0.8033     0.6374       1.02         10        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0161     0.0298    0.00856      0.003\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     15/215      6.96G     0.7915     0.6248      1.019         19        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0149      0.117       0.01    0.00296\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     16/215      6.96G     0.7849     0.6106      1.012          2        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0118     0.0298     0.0039    0.00128\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     17/215      6.96G     0.7422     0.5916     0.9887         44        800:  ^C\n",
+      "     17/215      6.96G     0.7432     0.5909     0.9886         49        800:  Traceback (most recent call last):\n",
+      "  File \"/root/.config/Ultralytics/DDP/_temp_f6tt1q5n139889764186704.py\", line 6, in <module>\n",
+      "    trainer.train()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 183, in train\n",
+      "    self._do_train(int(os.getenv(\"RANK\", -1)), world_size)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 284, in _do_train\n",
+      "    for i, batch in pbar:\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 628, in __next__\n",
+      "    data = self._next_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1316, in _next_data\n",
+      "    idx, data = self._get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1272, in _get_data\n",
+      "    success, data = self._try_get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1120, in _try_get_data\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1195, in __iter__\n",
+      "    data = self._data_queue.get(timeout=timeout)\n",
+      "  File \"/opt/conda/lib/python3.7/queue.py\", line 179, in get\n",
+      "    self.not_empty.wait(remaining)\n",
+      "  File \"/opt/conda/lib/python3.7/threading.py\", line 300, in wait\n",
+      "    gotit = waiter.acquire(True, timeout)\n",
+      "KeyboardInterrupt\n",
+      "    for obj in iterable:\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 628, in __next__\n",
+      "    data = self._next_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1316, in _next_data\n",
+      "    idx, data = self._get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1272, in _get_data\n",
+      "    success, data = self._try_get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1120, in _try_get_data\n",
+      "    data = self._data_queue.get(timeout=timeout)\n",
+      "  File \"/opt/conda/lib/python3.7/queue.py\", line 179, in get\n",
+      "    self.not_empty.wait(remaining)\n",
+      "  File \"/opt/conda/lib/python3.7/threading.py\", line 300, in wait\n",
+      "    gotit = waiter.acquire(True, timeout)\n",
+      "KeyboardInterrupt\n",
+      "\n",
+      "During handling of the above exception, another exception occurred:\n",
+      "\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/root/.config/Ultralytics/DDP/_temp_f6tt1q5n139889764186704.py\", line 6, in <module>\n",
+      "    trainer.train()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 183, in train\n",
+      "    self._do_train(int(os.getenv(\"RANK\", -1)), world_size)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 284, in _do_train\n",
+      "    for i, batch in pbar:\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1210, in __iter__\n",
+      "    self.close()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1316, in close\n",
+      "    self.display(pos=0)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1509, in display\n",
+      "    self.sp(self.__str__() if msg is None else msg)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 350, in print_status\n",
+      "    fp_write('\\r' + s + (' ' * max(last_len[0] - len_s, 0)))\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 343, in fp_write\n",
+      "    fp.write(_unicode(s))\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/utils.py\", line 145, in inner\n",
+      "    return func(*args, **kwargs)\n",
+      "KeyboardInterrupt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd {HOME}\n",
+    "\n",
+    "!yolo task=detect mode=train model=yolov8s.pt data={dataset.location}/data.yaml device = '0,1' epochs=215 imgsz=800 plots=True device=0,1 batch = 24\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.081618Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.082487Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.082244Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.082201Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!ls {HOME}/runs/detect/train/    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.083867Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.084678Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.084440Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.084414Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "Image(filename=f'{HOME}/runs/detect/train/confusion_matrix.png', width=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.086107Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.086926Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.086690Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.086663Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "Image(filename=f'{HOME}/runs/detect/train/results.png', width=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.088406Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.089394Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.089118Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.089088Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "Image(filename=f'{HOME}/runs/detect/train/val_batch0_pred.jpg', width=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.090791Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.091614Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.091379Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.091353Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "\n",
+    "!yolo task=detect mode=val model={HOME}/runs/detect/train/weights/best.pt data={dataset.location}/data.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.093044Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.093872Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.093627Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.093602Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "!yolo task=detect mode=predict model={HOME}/runs/detect/train/weights/best.pt conf=0.25 source={dataset.location}/test/images save=True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.095308Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.096145Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.095908Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.095881Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "from IPython.display import Image, display\n",
+    "\n",
+    "for image_path in glob.glob(f'{HOME}/runs/detect/predict3/*.jpg')[:3]:\n",
+    "      display(Image(filename=image_path, width=600))\n",
+    "      print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.097602Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.098464Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.098204Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.098177Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "project.version(dataset.version).deploy(model_type=\"yolov8\", model_path=f\"{HOME}/runs/detect/train/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.099771Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.100632Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.100401Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.100374Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Run inference on your model on a persistant, auto-scaling, cloud API\n",
+    "\n",
+    "#load model\n",
+    "model = project.version(dataset.version).model\n",
+    "\n",
+    "#choose random test set image\n",
+    "import os, random\n",
+    "test_set_loc = dataset.location + \"/test/images/\"\n",
+    "random_test_image = random.choice(os.listdir(test_set_loc))\n",
+    "print(\"running inference on \" + random_test_image)\n",
+    "\n",
+    "pred = model.predict(test_set_loc + random_test_image, confidence=40, overlap=30).json()\n",
+    "pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "909e94fa6c232d7c724ea0272d1d960c187d26acecb731545632ac2dfd18735f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebookcf133ce21c.ipynb

@@ -0,0 +1,804 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sat Mar  4 21:14:41 2023       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 531.18                 Driver Version: 531.18       CUDA Version: 12.1     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                      TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  NVIDIA GeForce GTX 1080 Ti    WDDM | 00000000:B3:00.0  On |                  N/A |\n",
+      "|100%   42C    P0               68W / 127W|   1980MiB / 11264MiB |      3%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|    0   N/A  N/A      1148    C+G   ... (x86)\\Audeze\\AudezeHQ\\AudezeHQ.exe    N/A      |\n",
+      "|    0   N/A  N/A      2232    C+G   ...GeForce Experience\\NVIDIA Share.exe    N/A      |\n",
+      "|    0   N/A  N/A      6208    C+G   ..._x64__rz1tebttyb220\\DolbyAccess.exe    N/A      |\n",
+      "|    0   N/A  N/A      9096    C+G   ...CBS_cw5n1h2txyewy\\TextInputHost.exe    N/A      |\n",
+      "|    0   N/A  N/A      9832    C+G   ...63.0_x86__zpdnekdrzrea0\\Spotify.exe    N/A      |\n",
+      "|    0   N/A  N/A     10856    C+G   ....0_x64__8wekyb3d8bbwe\\HxOutlook.exe    N/A      |\n",
+      "|    0   N/A  N/A     11512    C+G   ...2txyewy\\StartMenuExperienceHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     11596    C+G   ...GeForce Experience\\NVIDIA Share.exe    N/A      |\n",
+      "|    0   N/A  N/A     12780    C+G   ...rPicker\\PowerToys.ColorPickerUI.exe    N/A      |\n",
+      "|    0   N/A  N/A     14676    C+G   ...on\\110.0.1587.57\\msedgewebview2.exe    N/A      |\n",
+      "|    0   N/A  N/A     14900    C+G   C:\\Windows\\explorer.exe                   N/A      |\n",
+      "|    0   N/A  N/A     16116    C+G   ...FancyZones\\PowerToys.FancyZones.exe    N/A      |\n",
+      "|    0   N/A  N/A     17548    C+G   ...5n1h2txyewy\\ShellExperienceHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     17848      C   ...al\\Discord\\app-1.0.9011\\Discord.exe    N/A      |\n",
+      "|    0   N/A  N/A     18064    C+G   ...\\cef\\cef.win7x64\\steamwebhelper.exe    N/A      |\n",
+      "|    0   N/A  N/A     20132    C+G   ...crosoft\\Edge\\Application\\msedge.exe    N/A      |\n",
+      "|    0   N/A  N/A     20864    C+G   ...t.LockApp_cw5n1h2txyewy\\LockApp.exe    N/A      |\n",
+      "|    0   N/A  N/A     20928    C+G   ...B\\system_tray\\lghub_system_tray.exe    N/A      |\n",
+      "|    0   N/A  N/A     22896    C+G   ...Cloudflare WARP\\Cloudflare WARP.exe    N/A      |\n",
+      "|    0   N/A  N/A     23784    C+G   ...__8wekyb3d8bbwe\\WindowsTerminal.exe    N/A      |\n",
+      "|    0   N/A  N/A     24416    C+G   ...cal\\Microsoft\\OneDrive\\OneDrive.exe    N/A      |\n",
+      "|    0   N/A  N/A     27532    C+G   ...auncher\\PowerToys.PowerLauncher.exe    N/A      |\n",
+      "|    0   N/A  N/A     28696    C+G   ...siveControlPanel\\SystemSettings.exe    N/A      |\n",
+      "|    0   N/A  N/A     29184    C+G   ...ekyb3d8bbwe\\PhoneExperienceHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     32684    C+G   ...nt.CBS_cw5n1h2txyewy\\SearchHost.exe    N/A      |\n",
+      "|    0   N/A  N/A     34624    C+G   C:\\Program Files\\LGHUB\\lghub.exe          N/A      |\n",
+      "|    0   N/A  N/A     34692    C+G   ...pdnekdrzrea0\\XboxGameBarSpotify.exe    N/A      |\n",
+      "|    0   N/A  N/A     37692    C+G   ...4.0_x64__cv1g1gvanyjgm\\WhatsApp.exe    N/A      |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "HOME = os.getcwd()\n",
+    "print(HOME)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from IPython import display\n",
+    "display.clear_output()\n",
+    "from ultralytics import YOLO\n",
+    "ultralytics.checks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install roboflow\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'roboflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mroboflow\u001b[39;00m \u001b[39mimport\u001b[39;00m Roboflow\n\u001b[0;32m      2\u001b[0m rf \u001b[39m=\u001b[39m Roboflow(api_key\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m2NdQm1ivtFCAYiOLVTwn\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m project \u001b[39m=\u001b[39m rf\u001b[39m.\u001b[39mworkspace(\u001b[39m\"\u001b[39m\u001b[39mhackthethong\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mproject(\u001b[39m\"\u001b[39m\u001b[39mpothole-detection-gmnid\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'roboflow'"
+     ]
+    }
+   ],
+   "source": [
+    "from roboflow import Roboflow\n",
+    "rf = Roboflow(api_key=\"2NdQm1ivtFCAYiOLVTwn\")\n",
+    "project = rf.workspace(\"hackthethong\").project(\"pothole-detection-gmnid\")\n",
+    "dataset = project.version(3).download(\"yolov8\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:34.982368Z",
+     "iopub.status.busy": "2023-03-04T11:06:34.982065Z",
+     "iopub.status.idle": "2023-03-04T11:06:36.155978Z",
+     "shell.execute_reply": "2023-03-04T11:06:36.154454Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:34.982341Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sat Mar  4 11:06:35 2023       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 470.82.01    Driver Version: 470.82.01    CUDA Version: 11.4     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   36C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   1  Tesla T4            Off  | 00000000:00:05.0 Off |                    0 |\n",
+      "| N/A   47C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:36.165528Z",
+     "iopub.status.busy": "2023-03-04T11:06:36.162799Z",
+     "iopub.status.idle": "2023-03-04T11:06:36.175759Z",
+     "shell.execute_reply": "2023-03-04T11:06:36.174308Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:36.165476Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/working\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "HOME = os.getcwd()\n",
+    "print(HOME)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:36.180607Z",
+     "iopub.status.busy": "2023-03-04T11:06:36.179797Z",
+     "iopub.status.idle": "2023-03-04T11:06:55.605740Z",
+     "shell.execute_reply": "2023-03-04T11:06:55.604691Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:36.180564Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ultralytics YOLOv8.0.20 🚀 Python-3.7.12 torch-1.13.0 CUDA:0 (Tesla T4, 15110MiB)\n",
+      "Setup complete ✅ (2 CPUs, 15.6 GB RAM, 4437.1/8062.4 GB disk)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pip install method (recommended)\n",
+    "\n",
+    "!pip install ultralytics==8.0.20\n",
+    "\n",
+    "from IPython import display\n",
+    "display.clear_output()\n",
+    "\n",
+    "import ultralytics\n",
+    "ultralytics.checks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:55.608863Z",
+     "iopub.status.busy": "2023-03-04T11:06:55.608358Z",
+     "iopub.status.idle": "2023-03-04T11:06:55.615639Z",
+     "shell.execute_reply": "2023-03-04T11:06:55.613293Z",
+     "shell.execute_reply.started": "2023-03-04T11:06:55.608820Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ultralytics import YOLO\n",
+    "\n",
+    "from IPython.display import display, Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:06:55.618151Z",
+     "iopub.status.busy": "2023-03-04T11:06:55.617309Z",
+     "iopub.status.idle": "2023-03-04T11:07:53.787811Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/working/datasets\n",
+      "Collecting roboflow\n",
+      "  Downloading roboflow-0.2.32-py3-none-any.whl (50 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: opencv-python>=4.1.2 in /opt/conda/lib/python3.7/site-packages (from roboflow) (4.7.0.72)\n",
+      "Collecting wget\n",
+      "  Downloading wget-3.2.zip (10 kB)\n",
+      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25hRequirement already satisfied: urllib3>=1.26.6 in /opt/conda/lib/python3.7/site-packages (from roboflow) (1.26.14)\n",
+      "Requirement already satisfied: python-dotenv in /opt/conda/lib/python3.7/site-packages (from roboflow) (0.21.1)\n",
+      "Requirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.7/site-packages (from roboflow) (9.4.0)\n",
+      "Collecting chardet==4.0.0\n",
+      "  Downloading chardet-4.0.0-py2.py3-none-any.whl (178 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: tqdm>=4.41.0 in /opt/conda/lib/python3.7/site-packages (from roboflow) (4.64.1)\n",
+      "Collecting idna==2.10\n",
+      "  Downloading idna-2.10-py2.py3-none-any.whl (58 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from roboflow) (3.5.3)\n",
+      "Collecting cycler==0.10.0\n",
+      "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
+      "Collecting requests-toolbelt\n",
+      "  Downloading requests_toolbelt-0.10.1-py2.py3-none-any.whl (54 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: python-dateutil in /opt/conda/lib/python3.7/site-packages (from roboflow) (2.8.2)\n",
+      "Requirement already satisfied: certifi==2022.12.7 in /opt/conda/lib/python3.7/site-packages (from roboflow) (2022.12.7)\n",
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+      "Requirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.7/site-packages (from roboflow) (6.0)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from roboflow) (2.28.2)\n",
+      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from roboflow) (1.16.0)\n",
+      "Collecting pyparsing==2.4.7\n",
+      "  Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.7/site-packages (from roboflow) (1.4.4)\n",
+      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.3.1->roboflow) (4.4.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->roboflow) (4.38.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->roboflow) (23.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->roboflow) (2.1.1)\n",
+      "Building wheels for collected packages: wget\n",
+      "  Building wheel for wget (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9674 sha256=c21353920025ab2a7acd6c41f94a830e3de43131a8eb128b751e9613559978c7\n",
+      "  Stored in directory: /root/.cache/pip/wheels/e1/e8/db/ebe4dcd7d7d11208c1e4e4ef246cea4fcc8d463c93405a6555\n",
+      "Successfully built wget\n",
+      "Installing collected packages: wget, pyparsing, idna, cycler, chardet, requests-toolbelt, 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",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "librosa 0.10.0 requires soundfile>=0.12.1, but you have soundfile 0.11.0 which is incompatible.\n",
+      "cloud-tpu-client 0.10 requires google-api-python-client==1.8.0, but you have google-api-python-client 2.79.0 which is incompatible.\n",
+      "apache-beam 2.44.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.6 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0mSuccessfully installed chardet-4.0.0 cycler-0.10.0 idna-2.10 pyparsing-2.4.7 requests-toolbelt-0.10.1 roboflow-0.2.32 wget-3.2\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "loading Roboflow workspace...\n",
+      "loading Roboflow project...\n",
+      "Downloading Dataset Version Zip in Pothole-detection-1 to yolov8: 97% [254164992 / 260273386] bytes"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir {HOME}/datasets\n",
+    "%cd {HOME}/datasets\n",
+    "\n",
+    "!pip install roboflow\n",
+    "\n",
+    "from roboflow import Roboflow\n",
+    "rf = Roboflow(api_key=\"2NdQm1ivtFCAYiOLVTwn\")\n",
+    "project = rf.workspace(\"hackthethong\").project(\"pothole-detection-gmnid\")\n",
+    "dataset = project.version(1).download(\"yolov8\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-04T11:16:50.287812Z",
+     "iopub.status.busy": "2023-03-04T11:16:50.287079Z",
+     "iopub.status.idle": "2023-03-04T12:12:27.477858Z",
+     "shell.execute_reply": "2023-03-04T12:12:27.476221Z",
+     "shell.execute_reply.started": "2023-03-04T11:16:50.287774Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/working\n",
+      "Ultralytics YOLOv8.0.20 🚀 Python-3.7.12 torch-1.13.0 CUDA:0 (Tesla T4, 15110MiB)\n",
+      "                                                      CUDA:1 (Tesla T4, 15110MiB)\n",
+      "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.yaml, data=/kaggle/working/datasets/Pothole-detection-1/data.yaml, epochs=215, patience=50, batch=24, imgsz=800, save=True, cache=False, device=(0, 1), workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=ultralytics/assets/, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.001, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs/detect/train3\n",
+      "Overriding model.yaml nc=80 with nc=1\n",
+      "\n",
+      "                   from  n    params  module                                       arguments                     \n",
+      "  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 \n",
+      "  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
+      "  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             \n",
+      "  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
+      "  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
+      "  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
+      "  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           \n",
+      "  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              \n",
+      "  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           \n",
+      "  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 \n",
+      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 \n",
+      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
+      " 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
+      " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
+      " 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              \n",
+      " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 \n",
+      " 22        [15, 18, 21]  1   2116435  ultralytics.nn.modules.Detect                [1, [128, 256, 512]]          \n",
+      "Model summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs\n",
+      "\n",
+      "Transferred 349/355 items from pretrained weights\n",
+      "DDP settings: RANK 0, WORLD_SIZE 2, DEVICE cuda:0\n",
+      "Overriding model.yaml nc=80 with nc=1\n",
+      "\n",
+      "                   from  n    params  module                                       arguments                     \n",
+      "  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 \n",
+      "  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
+      "  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             \n",
+      "  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
+      "  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
+      "  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
+      "  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           \n",
+      "  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              \n",
+      "  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           \n",
+      "  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 \n",
+      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 \n",
+      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+      " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
+      " 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
+      " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
+      " 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              \n",
+      " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
+      " 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 \n",
+      " 22        [15, 18, 21]  1   2116435  ultralytics.nn.modules.Detect                [1, [128, 256, 512]]          \n",
+      "YOLOv8s summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs\n",
+      "\n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0011250000000000001), 63 bias\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/datasets/Pothole-detection-1/train/labels.cache.\u001b[0m\n",
+      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/datasets/Pothole-detection-1/valid/labels.cache...\u001b[0m\n",
+      "Image sizes 800 train, 800 val\n",
+      "Using 2 dataloader workers\n",
+      "Logging results to \u001b[1mruns/detect/train3\u001b[0m\n",
+      "Starting training for 215 epochs...\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      1/215      5.86G      3.481      4.528       4.09          7        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941   0.000902     0.0967   0.000495   0.000148\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      2/215      6.96G      3.266      3.481      3.476          2        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941   0.000937     0.0786   0.000536   0.000168\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      3/215      6.96G       2.43      2.349      2.433          7        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00105    0.00213    0.00035   0.000101\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      4/215      6.96G      1.867       1.71       1.77          8        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00488     0.0117     0.0025    0.00104\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      5/215      6.96G      1.549      1.331       1.48          3        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941      0.023     0.0266    0.00589     0.0015\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      6/215      6.96G      1.341      1.113       1.34         17        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00154    0.00531   0.000551   0.000166\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      7/215      6.96G      1.187     0.9461      1.234         14        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00854     0.0298    0.00488    0.00147\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      8/215      6.96G       1.08     0.8943      1.175          8        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0174     0.0765     0.0116    0.00329\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      9/215      6.96G      1.012     0.8116      1.133          5        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0058     0.0085    0.00295    0.00103\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     10/215      6.96G     0.9529      0.756      1.114          6        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0551     0.0351     0.0156    0.00493\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     11/215      6.96G     0.8946     0.7085      1.069          3        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941    0.00808      0.051    0.00986    0.00449\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     12/215      6.96G     0.8897     0.7045      1.057          4        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0929     0.0244     0.0159    0.00461\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     13/215      6.96G     0.8399     0.6641      1.048          5        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0321     0.0893     0.0204    0.00543\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     14/215      6.96G     0.8033     0.6374       1.02         10        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0161     0.0298    0.00856      0.003\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     15/215      6.96G     0.7915     0.6248      1.019         19        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0149      0.117       0.01    0.00296\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     16/215      6.96G     0.7849     0.6106      1.012          2        800: 1\n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        357        941     0.0118     0.0298     0.0039    0.00128\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "     17/215      6.96G     0.7422     0.5916     0.9887         44        800:  ^C\n",
+      "     17/215      6.96G     0.7432     0.5909     0.9886         49        800:  Traceback (most recent call last):\n",
+      "  File \"/root/.config/Ultralytics/DDP/_temp_f6tt1q5n139889764186704.py\", line 6, in <module>\n",
+      "    trainer.train()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 183, in train\n",
+      "    self._do_train(int(os.getenv(\"RANK\", -1)), world_size)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 284, in _do_train\n",
+      "    for i, batch in pbar:\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 628, in __next__\n",
+      "    data = self._next_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1316, in _next_data\n",
+      "    idx, data = self._get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1272, in _get_data\n",
+      "    success, data = self._try_get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1120, in _try_get_data\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1195, in __iter__\n",
+      "    data = self._data_queue.get(timeout=timeout)\n",
+      "  File \"/opt/conda/lib/python3.7/queue.py\", line 179, in get\n",
+      "    self.not_empty.wait(remaining)\n",
+      "  File \"/opt/conda/lib/python3.7/threading.py\", line 300, in wait\n",
+      "    gotit = waiter.acquire(True, timeout)\n",
+      "KeyboardInterrupt\n",
+      "    for obj in iterable:\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 628, in __next__\n",
+      "    data = self._next_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1316, in _next_data\n",
+      "    idx, data = self._get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1272, in _get_data\n",
+      "    success, data = self._try_get_data()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 1120, in _try_get_data\n",
+      "    data = self._data_queue.get(timeout=timeout)\n",
+      "  File \"/opt/conda/lib/python3.7/queue.py\", line 179, in get\n",
+      "    self.not_empty.wait(remaining)\n",
+      "  File \"/opt/conda/lib/python3.7/threading.py\", line 300, in wait\n",
+      "    gotit = waiter.acquire(True, timeout)\n",
+      "KeyboardInterrupt\n",
+      "\n",
+      "During handling of the above exception, another exception occurred:\n",
+      "\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/root/.config/Ultralytics/DDP/_temp_f6tt1q5n139889764186704.py\", line 6, in <module>\n",
+      "    trainer.train()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 183, in train\n",
+      "    self._do_train(int(os.getenv(\"RANK\", -1)), world_size)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/ultralytics/yolo/engine/trainer.py\", line 284, in _do_train\n",
+      "    for i, batch in pbar:\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1210, in __iter__\n",
+      "    self.close()\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1316, in close\n",
+      "    self.display(pos=0)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 1509, in display\n",
+      "    self.sp(self.__str__() if msg is None else msg)\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 350, in print_status\n",
+      "    fp_write('\\r' + s + (' ' * max(last_len[0] - len_s, 0)))\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/std.py\", line 343, in fp_write\n",
+      "    fp.write(_unicode(s))\n",
+      "  File \"/opt/conda/lib/python3.7/site-packages/tqdm/utils.py\", line 145, in inner\n",
+      "    return func(*args, **kwargs)\n",
+      "KeyboardInterrupt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd {HOME}\n",
+    "\n",
+    "!yolo task=detect mode=train model=yolov8s.pt data={dataset.location}/data.yaml device = '0,1' epochs=215 imgsz=800 plots=True device=0,1 batch = 24\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.081618Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.082487Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.082244Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.082201Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!ls {HOME}/runs/detect/train/    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.083867Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.084678Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.084440Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.084414Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "Image(filename=f'{HOME}/runs/detect/train/confusion_matrix.png', width=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.086107Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.086926Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.086690Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.086663Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "Image(filename=f'{HOME}/runs/detect/train/results.png', width=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.088406Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.089394Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.089118Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.089088Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "Image(filename=f'{HOME}/runs/detect/train/val_batch0_pred.jpg', width=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.090791Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.091614Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.091379Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.091353Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "\n",
+    "!yolo task=detect mode=val model={HOME}/runs/detect/train/weights/best.pt data={dataset.location}/data.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.093044Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.093872Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.093627Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.093602Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%cd {HOME}\n",
+    "!yolo task=detect mode=predict model={HOME}/runs/detect/train/weights/best.pt conf=0.25 source={dataset.location}/test/images save=True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.095308Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.096145Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.095908Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.095881Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "from IPython.display import Image, display\n",
+    "\n",
+    "for image_path in glob.glob(f'{HOME}/runs/detect/predict3/*.jpg')[:3]:\n",
+    "      display(Image(filename=image_path, width=600))\n",
+    "      print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.097602Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.098464Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.098204Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.098177Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "project.version(dataset.version).deploy(model_type=\"yolov8\", model_path=f\"{HOME}/runs/detect/train/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-03-04T12:12:29.099771Z",
+     "iopub.status.idle": "2023-03-04T12:12:29.100632Z",
+     "shell.execute_reply": "2023-03-04T12:12:29.100401Z",
+     "shell.execute_reply.started": "2023-03-04T12:12:29.100374Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Run inference on your model on a persistant, auto-scaling, cloud API\n",
+    "\n",
+    "#load model\n",
+    "model = project.version(dataset.version).model\n",
+    "\n",
+    "#choose random test set image\n",
+    "import os, random\n",
+    "test_set_loc = dataset.location + \"/test/images/\"\n",
+    "random_test_image = random.choice(os.listdir(test_set_loc))\n",
+    "print(\"running inference on \" + random_test_image)\n",
+    "\n",
+    "pred = model.predict(test_set_loc + random_test_image, confidence=40, overlap=30).json()\n",
+    "pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "909e94fa6c232d7c724ea0272d1d960c187d26acecb731545632ac2dfd18735f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

y8best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:122d9c0094f56086015e691252ec35b6492c8ded346115da1eeac0f6a35c31c1
+size 136723528