├── Cars ├── 07car1.jpg ├── 1-corvette-stingray-c8-2019-fd-hr-hero-front_0.jpg ├── 15-classic-cars-that-define-cool-hero-1000x600.jpg ├── 1_title_2020_mazda_miata.jpg ├── 2018_Toyota_Corolla_(ZRE172R)_Ascent_sedan_(2018-11-02)_01.jpg ├── 2020-audi-rs7-112-1569274021.jpg ├── 57263_2020_Mercedes_Benz_GLS.jpg ├── 6.jpg ├── 861a4abe_2fec_44aa_aa21_c79ceca1cdc3_660_080520125004.jpg ├── 960x0.jpg ├── BlogFeaturedImage_Square_Car-Modifications-01.jpg ├── Buy-a-Kia-Telluride-Instead-gear-patrol-slide-1.jpg ├── SONATA-hero-option1-764A5360-edit.jpg ├── Transpo_G70_TA-518126.jpg ├── b6-18-e1579808811394.jpg ├── bmw-vantablack-car-design_dezeen_2364_sq-1.jpg ├── bugatti-chiron-pur-sport-106-1582836604.jpg ├── carbon-fiber-shelby-mustang-1600685276.jpg ├── download (1).jfif ├── download (2).jfif ├── download.jfif ├── images (1).jfif ├── images (2).jfif ├── images.jfif ├── lamborghini_660_140220101539.jpg ├── large-2479-s-classsaloon.jpg ├── mahindra-tuv300-1.jpg ├── modified-honda-civic-in-kerela-blue-rear-angle-2.jpg ├── performance-mobile-new.jpg ├── photo-1552519507-da3b142c6e3d.jfif ├── rear-ends-31.jpg ├── red-race-car-back-low-angle-view-isolated-white-background-88933177.jpg ├── renault-city-k-ze-front-view0.jpeg ├── rrswbphev006.jpg ├── se-image-56e1019239c2823a80a6d2c54244f4e0.jpg ├── se-image-c4a9cdcf0a58353aa5323d397bde7f34.jpg ├── tata-tiago-1.jpg ├── vehicle_1404.jpg ├── volkswagen-polo-front-angle-red.jpg └── xuv300-exterior-rear-view.jpeg ├── Cricket ball ├── 1200px-A_Cricket_ball.jpg ├── 14451_1024x1024.jpg ├── 4-cut-piece-leather-cricket-ball-500x500.jpg ├── 4125d5RJ+zL._SX425_.jpg ├── 41Rn9CCo-rL._SX425_.jpg ├── 519sC7Eti8L._SX425_.jpg ├── 51PychlrKSL._SL1001_.jpg ├── 51QW4OnVU6L._SL1100_.jpg ├── 8128AntzEqL._AC_SL1500_.jpg ├── CDAK17Ball_20Crest_20Elite_20Red_20Back_1500x.jpg ├── CDBK15Ball_20Velocity_20Ball_20Red_1500x.jpg ├── FLASH-Mens-Synthetic-Cricket-Ball-Orange.jpeg ├── SG_CB_000083_large.jpg ├── UTB80amexrnJXKJkSahGq6xhzFXaV.jpg ├── WhatsApp_Image_2019-08-17_at_6.29.43_PM_4ef3d4d4-f887-4256-90b5-e052b56969b2_619x.jpeg ├── cotton-legging-500x500.jpg ├── cricket-ball_1024x.jpg.crdownload ├── cricket-leather-ball-500x500.jpg ├── download (1).jfif ├── download.jfif ├── dukesinternational.jpg ├── eco-friendly-leather-cricket-ball-821.jpg ├── images (1).jfif ├── images (2).jfif ├── images (3).jfif ├── images (4).jfif ├── images.jfif ├── p1578877.jpg ├── poly_soft_ball.jpg └── r0_0_800_600_w1200_h678_fmax.jpg ├── Ice cream cone ├── 02wmt-articleLarge-v3.jpg ├── 220px-Strawberry_ice_cream_cone_(5076899310).jpg ├── 40194016_1-vadilal-flingo-ice-cream-cone-nutty-butter-scotch.jpg ├── 59920482-chocolate-ice-cream-cone-isolated.jpg ├── All-Time-Favourite---Soya-Ice-Cream-Cone-compressor.jpg ├── Butterscotch-Cone-Ice-Cream.jpg ├── IceCreamCone13.jpg ├── Mini-Frozen-Treat-Pops1.jpg ├── big-cone-ice-cream-500x500.jpg ├── black-cone-7.jpg ├── chocolate-ice-cream-cone-blue-faded-pastel-color-background-chocolate-ice-cream-cone-blue-faded-pastel-color-background-hand-109789561.jpg ├── chocolate-ice-cream-cone-duckycards.jpg ├── depositphotos_15875059-stock-photo-ice-cream.jpg ├── havmor-magic-ice-cream-cones-500x500.jpg ├── ice-cream-cone-cupcakes-4-735x1101.jpg ├── ice-cream-cone-on-pink-background-kevinruss.jpg ├── ice_cream_cone_fancy2.jpg ├── images (1).jfif ├── images (2).jfif ├── images (3).jfif ├── images (4).jfif ├── images (5).jfif ├── images (6).jfif ├── images.jfif ├── istockphoto-583839158-612x612.jpg ├── maxresdefault.jpg ├── melting-strawberry-ice-cream-cone-kevinruss.jpg ├── mini-ice-cream-cone-500x500.jpg ├── strawberry-ice-cream-cone-250x250.jpg └── t-mcdonalds-Vanilla-Reduced-Fat-Ice-Cream-Cone.jpg ├── README.md └── Shanmukh_Classification.ipynb /Cars/07car1.jpg: -------------------------------------------------------------------------------- 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You can classify any category images. 4 | Here i have used Cars, Ice cream cone and Cricket ball images for classification, but you can use any category images to classify, all the steps are mentioned in the google collab file. 5 | Cars , Ice cream cone and Cricket ball images are shared in this github repository. 6 | -------------------------------------------------------------------------------- /Shanmukh_Classification.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Shanmukh-Classification.ipynb", 7 | "provenance": [], 8 | "toc_visible": true, 9 | "mount_file_id": "1f0XtS7FMhgrj2FnqPssOI3tsovKoVeXM", 10 | "authorship_tag": "ABX9TyNXoByyuzJc8pEa+Ia/zOiv", 11 | "include_colab_link": true 12 | }, 13 | "kernelspec": { 14 | "name": "python3", 15 | "display_name": "Python 3" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": { 32 | "id": "uBajLXYrWBlo" 33 | }, 34 | "source": [ 35 | "Importing all the required packages" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "metadata": { 41 | "id": "VgPd1fjcOKlg" 42 | }, 43 | "source": [ 44 | "\r\n", 45 | "import pandas as pd\r\n", 46 | "from sklearn import svm\r\n", 47 | "from sklearn.model_selection import GridSearchCV\r\n", 48 | "import os\r\n", 49 | "import matplotlib.pyplot as plt\r\n", 50 | "from skimage.transform import resize\r\n", 51 | "from skimage.io import imread\r\n", 52 | "import numpy as np\r\n", 53 | "from sklearn.model_selection import train_test_split\r\n", 54 | "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix\r\n", 55 | "import pickle\r\n", 56 | "\r\n" 57 | ], 58 | "execution_count": 1, 59 | "outputs": [] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": { 64 | "id": "i8zhtW8yz34D" 65 | }, 66 | "source": [ 67 | "**NOTE** : Please enter Category Names same as folder name" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "metadata": { 73 | "colab": { 74 | "base_uri": "https://localhost:8080/" 75 | }, 76 | "id": "umZtNj4CTllG", 77 | "outputId": "6bd8a057-f089-41e9-e55e-ef7d8fe47639" 78 | }, 79 | "source": [ 80 | "Categories=['Cars','Ice cream cone','Cricket ball']\r\n", 81 | "print(\"Type y to give categories or type n to go with classification of Cars,Ice Cream cone and Cricket ball\");\r\n", 82 | "\r\n", 83 | "while(True):\r\n", 84 | " check=input()\r\n", 85 | " if(check=='n' or check=='y'):\r\n", 86 | " break\r\n", 87 | " print(\"Please give a valid input (y/n)\")\r\n", 88 | "if(check=='y'):\r\n", 89 | " print(\"Enter How Many types of Images do you want to classify\")\r\n", 90 | " n=int(input())\r\n", 91 | " Categories=[]\r\n", 92 | " print(f'please enter {n} names')\r\n", 93 | " for i in range(n):\r\n", 94 | " name=input()\r\n", 95 | " Categories.append(name)\r\n", 96 | " print(f\"If not drive Please upload all the {n} category images in google collab with the same names as given in categories\")\r\n", 97 | "\r\n" 98 | ], 99 | "execution_count": 5, 100 | "outputs": [ 101 | { 102 | "output_type": "stream", 103 | "text": [ 104 | "Type y to give categories or type n to go with classification of Cars,Ice Cream cone and Cricket ball\n", 105 | "y\n", 106 | "Enter How Many types of Images do you want to classify\n", 107 | "3\n", 108 | "please enter 3 names\n", 109 | "Cars\n", 110 | "Ice cream cone\n", 111 | "Cricket ball\n", 112 | "If not drive Please upload all the 3 category images in google collab with the same names as given in categories\n" 113 | ], 114 | "name": "stdout" 115 | } 116 | ] 117 | }, 118 | { 119 | "cell_type": "markdown", 120 | "metadata": { 121 | "id": "0wwiPscqYune" 122 | }, 123 | "source": [ 124 | "Loading all the images and creating a DataFrame\r\n", 125 | "\r\n", 126 | "If you have your images in your google drive, simply mount the google Drive and copy the path of the folder containing all the Category images and Change the datadir variable to that path" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "metadata": { 132 | "colab": { 133 | "base_uri": "https://localhost:8080/", 134 | "height": 531 135 | }, 136 | "id": "XdGD-XpMYUjJ", 137 | "outputId": "d88292e8-0fd2-4c41-a485-c98045228f62" 138 | }, 139 | "source": [ 140 | "flat_data_arr=[]\r\n", 141 | "target_arr=[]\r\n", 142 | "#please use datadir='/content' if the files are upload on to google collab\r\n", 143 | "#else mount the drive and give path of the parent-folder containing all category images folders.\r\n", 144 | "datadir='/content/drive/MyDrive/ML'\r\n", 145 | "for i in Categories:\r\n", 146 | " print(f'loading... category : {i}')\r\n", 147 | " path=os.path.join(datadir,i)\r\n", 148 | " for img in os.listdir(path):\r\n", 149 | " img_array=imread(os.path.join(path,img))\r\n", 150 | " img_resized=resize(img_array,(150,150,3))\r\n", 151 | " flat_data_arr.append(img_resized.flatten())\r\n", 152 | " target_arr.append(Categories.index(i))\r\n", 153 | " print(f'loaded category:{i} successfully')\r\n", 154 | "flat_data=np.array(flat_data_arr)\r\n", 155 | "target=np.array(target_arr)\r\n", 156 | "df=pd.DataFrame(flat_data)\r\n", 157 | "df['Target']=target\r\n", 158 | "df" 159 | ], 160 | "execution_count": 6, 161 | "outputs": [ 162 | { 163 | "output_type": "stream", 164 | "text": [ 165 | "loading... category : Cars\n", 166 | "loaded category:Cars successfully\n", 167 | "loading... category : Ice cream cone\n", 168 | "loaded category:Ice cream cone successfully\n", 169 | "loading... category : Cricket ball\n", 170 | "loaded category:Cricket ball successfully\n" 171 | ], 172 | "name": "stdout" 173 | }, 174 | { 175 | "output_type": "execute_result", 176 | "data": { 177 | "text/html": [ 178 | "
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100 rows × 67501 columns

\n", 1207 | "
" 1208 | ], 1209 | "text/plain": [ 1210 | " 0 1 2 ... 67498 67499 Target\n", 1211 | "0 0.874510 0.827529 0.796078 ... 0.360784 0.462837 0\n", 1212 | "1 0.843137 0.843137 0.835294 ... 0.125490 0.078431 0\n", 1213 | "2 0.105882 0.094118 0.121569 ... 0.117647 0.121569 0\n", 1214 | "3 0.488523 0.764706 0.894118 ... 0.568627 0.474797 0\n", 1215 | "4 0.910196 0.910196 0.910196 ... 0.912941 0.912941 0\n", 1216 | ".. ... ... ... ... ... ... ...\n", 1217 | "95 0.933333 0.937255 0.956863 ... 0.937255 0.956863 2\n", 1218 | "96 1.000000 1.000000 1.000000 ... 1.000000 1.000000 2\n", 1219 | "97 1.000000 1.000000 1.000000 ... 1.000000 1.000000 2\n", 1220 | "98 0.607490 0.560379 0.368641 ... 0.690222 0.026634 2\n", 1221 | "99 0.988235 0.988235 0.980392 ... 0.988235 0.980392 2\n", 1222 | "\n", 1223 | "[100 rows x 67501 columns]" 1224 | ] 1225 | }, 1226 | "metadata": { 1227 | "tags": [] 1228 | }, 1229 | "execution_count": 6 1230 | } 1231 | ] 1232 | }, 1233 | { 1234 | "cell_type": "markdown", 1235 | "metadata": { 1236 | "id": "gUp5FDqIY070" 1237 | }, 1238 | "source": [ 1239 | "Splitting the data into training and testing data" 1240 | ] 1241 | }, 1242 | { 1243 | "cell_type": "code", 1244 | "metadata": { 1245 | "colab": { 1246 | "base_uri": "https://localhost:8080/" 1247 | }, 1248 | "id": "Jg3XHRP3OUyf", 1249 | "outputId": "f9f0a456-8a4c-45e6-9b01-509d753139f2" 1250 | }, 1251 | "source": [ 1252 | "x=df.iloc[:,:-1]\r\n", 1253 | "y=df.iloc[:,-1]\r\n", 1254 | "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=77,stratify=y)\r\n", 1255 | "print('Splitted Successfully')" 1256 | ], 1257 | "execution_count": 7, 1258 | "outputs": [ 1259 | { 1260 | "output_type": "stream", 1261 | "text": [ 1262 | "Splitted Successfully\n" 1263 | ], 1264 | "name": "stdout" 1265 | } 1266 | ] 1267 | }, 1268 | { 1269 | "cell_type": "markdown", 1270 | "metadata": { 1271 | "id": "YMmSZWDJY_eE" 1272 | }, 1273 | "source": [ 1274 | "This Part of code may take a while for training the data using SVC model" 1275 | ] 1276 | }, 1277 | { 1278 | "cell_type": "code", 1279 | "metadata": { 1280 | "colab": { 1281 | "base_uri": "https://localhost:8080/" 1282 | }, 1283 | "id": "DIaIT2GlOvy6", 1284 | "outputId": "47a22b93-17d2-4f2a-fb1f-a0e355551852" 1285 | }, 1286 | "source": [ 1287 | "param_grid={'C':[0.1,1,10,100],'gamma':[0.0001,0.001,0.1,1],'kernel':['rbf','poly']}\r\n", 1288 | "svc=svm.SVC(probability=True)\r\n", 1289 | "print(\"The training of the model is started, please wait for while as it may take few minutes to complete\")\r\n", 1290 | "model=GridSearchCV(svc,param_grid)\r\n", 1291 | "model.fit(x_train,y_train)\r\n", 1292 | "print('The Model is trained well with the given images')\r\n", 1293 | "model.best_params_" 1294 | ], 1295 | "execution_count": 8, 1296 | "outputs": [ 1297 | { 1298 | "output_type": "stream", 1299 | "text": [ 1300 | "The training of the model is started, please wait for while as it may take few minutes to complete\n", 1301 | "The Model is trained well with the given images\n" 1302 | ], 1303 | "name": "stdout" 1304 | }, 1305 | { 1306 | "output_type": "execute_result", 1307 | "data": { 1308 | "text/plain": [ 1309 | "{'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}" 1310 | ] 1311 | }, 1312 | "metadata": { 1313 | "tags": [] 1314 | }, 1315 | "execution_count": 8 1316 | } 1317 | ] 1318 | }, 1319 | { 1320 | "cell_type": "markdown", 1321 | "metadata": { 1322 | "id": "VWKN2_4eZZmF" 1323 | }, 1324 | "source": [ 1325 | "Predicting our testing data" 1326 | ] 1327 | }, 1328 | { 1329 | "cell_type": "code", 1330 | "metadata": { 1331 | "colab": { 1332 | "base_uri": "https://localhost:8080/" 1333 | }, 1334 | "id": "h7qEbNLoSAcS", 1335 | "outputId": "3c93b66c-9202-42ee-ae30-5cadea7f00ef" 1336 | }, 1337 | "source": [ 1338 | "y_pred=model.predict(x_test)\r\n", 1339 | "print(\"The predicted Data is :\")\r\n", 1340 | "y_pred" 1341 | ], 1342 | "execution_count": 9, 1343 | "outputs": [ 1344 | { 1345 | "output_type": "stream", 1346 | "text": [ 1347 | "The predicted Data is :\n" 1348 | ], 1349 | "name": "stdout" 1350 | }, 1351 | { 1352 | "output_type": "execute_result", 1353 | "data": { 1354 | "text/plain": [ 1355 | "array([0, 0, 2, 2, 2, 1, 1, 2, 0, 1, 2, 2, 1, 1, 2, 2, 0, 0, 1, 1])" 1356 | ] 1357 | }, 1358 | "metadata": { 1359 | "tags": [] 1360 | }, 1361 | "execution_count": 9 1362 | } 1363 | ] 1364 | }, 1365 | { 1366 | "cell_type": "code", 1367 | "metadata": { 1368 | "colab": { 1369 | "base_uri": "https://localhost:8080/" 1370 | }, 1371 | "id": "A4rMJQ0HSCgU", 1372 | "outputId": "c5a922e2-4c75-4f95-96e3-bf9d6c9a7089" 1373 | }, 1374 | "source": [ 1375 | "print(\"The actual data is:\")\r\n", 1376 | "np.array(y_test)" 1377 | ], 1378 | "execution_count": 10, 1379 | "outputs": [ 1380 | { 1381 | "output_type": "stream", 1382 | "text": [ 1383 | "The actual data is:\n" 1384 | ], 1385 | "name": "stdout" 1386 | }, 1387 | { 1388 | "output_type": "execute_result", 1389 | "data": { 1390 | "text/plain": [ 1391 | "array([0, 0, 2, 2, 0, 1, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 0, 0, 1, 1])" 1392 | ] 1393 | }, 1394 | "metadata": { 1395 | "tags": [] 1396 | }, 1397 | "execution_count": 10 1398 | } 1399 | ] 1400 | }, 1401 | { 1402 | "cell_type": "code", 1403 | "metadata": { 1404 | "colab": { 1405 | "base_uri": "https://localhost:8080/" 1406 | }, 1407 | "id": "ZwXKKc5FSFMf", 1408 | "outputId": "d6c6e632-2585-4405-87b6-4b2321e41298" 1409 | }, 1410 | "source": [ 1411 | "#classification_report(y_pred,y_test)\r\n", 1412 | "print(f\"The model is {accuracy_score(y_pred,y_test)*100}% accurate\")\r\n", 1413 | "#confusion_matrix(y_pred,y_test)" 1414 | ], 1415 | "execution_count": 11, 1416 | "outputs": [ 1417 | { 1418 | "output_type": "stream", 1419 | "text": [ 1420 | "The model is 85.0% accurate\n" 1421 | ], 1422 | "name": "stdout" 1423 | } 1424 | ] 1425 | }, 1426 | { 1427 | "cell_type": "markdown", 1428 | "metadata": { 1429 | "id": "B8rpwj-yX4hl" 1430 | }, 1431 | "source": [ 1432 | "Using Pickle to save the model to disk" 1433 | ] 1434 | }, 1435 | { 1436 | "cell_type": "code", 1437 | "metadata": { 1438 | "colab": { 1439 | "base_uri": "https://localhost:8080/" 1440 | }, 1441 | "id": "Jf8xp5BYp_7E", 1442 | "outputId": "bb161fc8-afc1-4f80-87a6-c4859708c0fc" 1443 | }, 1444 | "source": [ 1445 | "pickle.dump(model,open('img_model.p','wb'))\r\n", 1446 | "print(\"Pickle is dumped successfully\")" 1447 | ], 1448 | "execution_count": 12, 1449 | "outputs": [ 1450 | { 1451 | "output_type": "stream", 1452 | "text": [ 1453 | "Pickle is dumped successfully\n" 1454 | ], 1455 | "name": "stdout" 1456 | } 1457 | ] 1458 | }, 1459 | { 1460 | "cell_type": "markdown", 1461 | "metadata": { 1462 | "id": "D5j6KOPCZ8av" 1463 | }, 1464 | "source": [ 1465 | "The Machine-Learning Based Model is Created Successfully. Now You can test for classification of any image which falls in the mentioned Categories\r\n", 1466 | "\r\n", 1467 | "Testing of model :" 1468 | ] 1469 | }, 1470 | { 1471 | "cell_type": "code", 1472 | "metadata": { 1473 | "id": "6MVmsPLdS6-X", 1474 | "colab": { 1475 | "base_uri": "https://localhost:8080/", 1476 | "height": 427 1477 | }, 1478 | "outputId": "56a836cf-2eca-4111-d585-34697dd71db5" 1479 | }, 1480 | "source": [ 1481 | "#print(os.path.abspath(os.getcwd()))\r\n", 1482 | "model=pickle.load(open('img_model.p','rb'))\r\n", 1483 | "\r\n", 1484 | "url=input('Enter URL of Image')\r\n", 1485 | "img=imread(url)\r\n", 1486 | "plt.imshow(img)\r\n", 1487 | "plt.show()\r\n", 1488 | "img_resize=resize(img,(150,150,3))\r\n", 1489 | "l=[img_resize.flatten()]\r\n", 1490 | "probability=model.predict_proba(l)\r\n", 1491 | "for ind,val in enumerate(Categories):\r\n", 1492 | " print(f'{val} = {probability[0][ind]*100}%')\r\n", 1493 | "print(\"The predicted image is : \"+Categories[model.predict(l)[0]])\r\n", 1494 | "print(f'Is the image a {Categories[model.predict(l)[0]]} ?(y/n)')\r\n", 1495 | "while(True):\r\n", 1496 | " b=input()\r\n", 1497 | " if(b==\"y\" or b==\"n\"):\r\n", 1498 | " break\r\n", 1499 | " print(\"please enter either y or n\")\r\n", 1500 | "\r\n", 1501 | "if(b=='n'):\r\n", 1502 | " print(\"What is the image?\")\r\n", 1503 | " for i in range(len(Categories)):\r\n", 1504 | " print(f\"Enter {i} for {Categories[i]}\")\r\n", 1505 | " k=int(input())\r\n", 1506 | " while(k<0 or k>=len(Categories)):\r\n", 1507 | " print(f\"Please enter a valid number between 0-{len(Categories)-1}\")\r\n", 1508 | " k=int(input())\r\n", 1509 | " print(\"Please wait for a while for the model to learn from this image :)\")\r\n", 1510 | " flat_arr=flat_data_arr.copy()\r\n", 1511 | " tar_arr=target_arr.copy()\r\n", 1512 | " tar_arr.append(k)\r\n", 1513 | " flat_arr.extend(l)\r\n", 1514 | " tar_arr=np.array(tar_arr)\r\n", 1515 | " flat_df=np.array(flat_arr)\r\n", 1516 | " df1=pd.DataFrame(flat_df)\r\n", 1517 | " df1['Target']=tar_arr\r\n", 1518 | " model1=GridSearchCV(svc,param_grid)\r\n", 1519 | " x1=df1.iloc[:,:-1]\r\n", 1520 | " y1=df1.iloc[:,-1]\r\n", 1521 | " x_train1,x_test1,y_train1,y_test1=train_test_split(x1,y1,test_size=0.20,random_state=77,stratify=y1)\r\n", 1522 | " d={}\r\n", 1523 | " for i in model.best_params_:\r\n", 1524 | " d[i]=[model.best_params_[i]]\r\n", 1525 | " model1=GridSearchCV(svc,d)\r\n", 1526 | " model1.fit(x_train1,y_train1)\r\n", 1527 | " y_pred1=model.predict(x_test1)\r\n", 1528 | " print(f\"The model is now {accuracy_score(y_pred1,y_test1)*100}% accurate\")\r\n", 1529 | " pickle.dump(model1,open('img_model.p','wb'))\r\n", 1530 | "print(\"Thank you for your feedback\")\r\n" 1531 | ], 1532 | "execution_count": 27, 1533 | "outputs": [ 1534 | { 1535 | "output_type": "stream", 1536 | "text": [ 1537 | "Enter URL of Imagehttps://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQSQxhmybBg29I3iWeWAtlqO0D2Y_A65nhwW9E-OrGipYVL39woCkGzOspp&s=10\n" 1538 | ], 1539 | "name": "stdout" 1540 | }, 1541 | { 1542 | "output_type": "display_data", 1543 | "data": { 1544 | "image/png": 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\n", 1545 | "text/plain": [ 1546 | "
" 1547 | ] 1548 | }, 1549 | "metadata": { 1550 | "tags": [], 1551 | "needs_background": "light" 1552 | } 1553 | }, 1554 | { 1555 | "output_type": "stream", 1556 | "text": [ 1557 | "Cars = 4.233077822970056%\n", 1558 | "Ice cream cone = 88.71371028584343%\n", 1559 | "Cricket ball = 7.053211891186489%\n", 1560 | "The predicted image is : Ice cream cone\n", 1561 | "Is the image a Ice cream cone ?(y/n)\n", 1562 | "y\n", 1563 | "Thank you for your feedback\n" 1564 | ], 1565 | "name": "stdout" 1566 | } 1567 | ] 1568 | }, 1569 | { 1570 | "cell_type": "markdown", 1571 | "metadata": { 1572 | "id": "fJ_t-zqIsTCA" 1573 | }, 1574 | "source": [ 1575 | "This model learns from its mistake and rectifies them and does not repeat the same mistake again ;)" 1576 | ] 1577 | } 1578 | ] 1579 | } --------------------------------------------------------------------------------