--- date: 2020-01-16 10:36 description: Tutorial on creating a custom Image Classifier using Turicreate and a dataset from Kaggle tags: Tutorial, Colab, Turicreate --- # Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire *For setting up Kaggle with Google Colab, please refer to my previous post* ## Dataset ### Mounting Google Drive ```python import os from google.colab import drive drive.mount('/content/drive') ``` ### Downloading Dataset from Kaggle ```python os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/" !kaggle datasets download ashutosh69/fire-and-smoke-dataset !unzip "fire-and-smoke-dataset.zip" ``` ## Pre-Processing ```Termcap !mkdir default smoke fire ``` \ ```Termcap !ls data/data/img_data/train/default/*.jpg ``` \ ```Termcap img_1002.jpg img_20.jpg img_519.jpg img_604.jpg img_80.jpg img_1003.jpg img_21.jpg img_51.jpg img_60.jpg img_8.jpg img_1007.jpg img_22.jpg img_520.jpg img_61.jpg img_900.jpg img_100.jpg img_23.jpg img_521.jpg 'img_62 (2).jpg' img_920.jpg img_1014.jpg img_24.jpg 'img_52 (2).jpg' img_62.jpg img_921.jpg img_1018.jpg img_29.jpg img_522.jpg 'img_63 (2).jpg' img_922.jpg img_101.jpg img_3000.jpg img_523.jpg img_63.jpg img_923.jpg img_1027.jpg img_335.jpg img_524.jpg img_66.jpg img_924.jpg img_102.jpg img_336.jpg img_52.jpg img_67.jpg img_925.jpg img_1042.jpg img_337.jpg img_530.jpg img_68.jpg img_926.jpg img_1043.jpg img_338.jpg img_531.jpg img_700.jpg img_927.jpg img_1046.jpg img_339.jpg 'img_53 (2).jpg' img_701.jpg img_928.jpg img_1052.jpg img_340.jpg img_532.jpg img_702.jpg img_929.jpg img_107.jpg img_341.jpg img_533.jpg img_703.jpg img_930.jpg img_108.jpg img_3.jpg img_537.jpg img_704.jpg img_931.jpg img_109.jpg img_400.jpg img_538.jpg img_705.jpg img_932.jpg img_10.jpg img_471.jpg img_539.jpg img_706.jpg img_933.jpg img_118.jpg img_472.jpg img_53.jpg img_707.jpg img_934.jpg img_12.jpg img_473.jpg img_540.jpg img_708.jpg img_935.jpg img_14.jpg img_488.jpg img_541.jpg img_709.jpg img_938.jpg img_15.jpg img_489.jpg 'img_54 (2).jpg' img_70.jpg img_958.jpg img_16.jpg img_490.jpg img_542.jpg img_710.jpg img_971.jpg img_17.jpg img_491.jpg img_543.jpg 'img_71 (2).jpg' img_972.jpg img_18.jpg img_492.jpg img_54.jpg img_71.jpg img_973.jpg img_19.jpg img_493.jpg 'img_55 (2).jpg' img_72.jpg img_974.jpg img_1.jpg img_494.jpg img_55.jpg img_73.jpg img_975.jpg img_200.jpg img_495.jpg img_56.jpg img_74.jpg img_980.jpg img_201.jpg img_496.jpg img_57.jpg img_75.jpg img_988.jpg img_202.jpg img_497.jpg img_58.jpg img_76.jpg img_9.jpg img_203.jpg img_4.jpg img_59.jpg img_77.jpg img_204.jpg img_501.jpg img_601.jpg img_78.jpg img_205.jpg img_502.jpg img_602.jpg img_79.jpg img_206.jpg img_50.jpg img_603.jpg img_7.jpg ``` The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate ```python from PIL import Image import glob folders = ["default","smoke","fire"] for folder in folders: n = 1 for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"): im = Image.open(file) rgb_im = im.convert('RGB') rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100) n +=1 for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"): im = Image.open(file) rgb_im = im.convert('RGB') rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100) n +=1 ``` \ ```Termcap !mkdir train !mv default ./train !mv smoke ./train !mv fire ./train ``` ## Making the Image Classifier ### Making an SFrame ```Termcap !pip install turicreate ``` \ ```python import turicreate as tc import os data = tc.image_analysis.load_images("./train", with_path=True) data["label"] = data["path"].apply(lambda path: os.path.basename(os.path.dirname(path))) print(data) data.save('fire-smoke.sframe') ``` \ ```Termcap +-------------------------+------------------------+ | path | image | +-------------------------+------------------------+ | ./train/default/1.jpg | Height: 224 Width: 224 | | ./train/default/10.jpg | Height: 224 Width: 224 | | ./train/default/100.jpg | Height: 224 Width: 224 | | ./train/default/101.jpg | Height: 224 Width: 224 | | ./train/default/102.jpg | Height: 224 Width: 224 | | ./train/default/103.jpg | Height: 224 Width: 224 | | ./train/default/104.jpg | Height: 224 Width: 224 | | ./train/default/105.jpg | Height: 224 Width: 224 | | ./train/default/106.jpg | Height: 224 Width: 224 | | ./train/default/107.jpg | Height: 224 Width: 224 | +-------------------------+------------------------+ [2028 rows x 2 columns] Note: Only the head of the SFrame is printed. You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns. +-------------------------+------------------------+---------+ | path | image | label | +-------------------------+------------------------+---------+ | ./train/default/1.jpg | Height: 224 Width: 224 | default | | ./train/default/10.jpg | Height: 224 Width: 224 | default | | ./train/default/100.jpg | Height: 224 Width: 224 | default | | ./train/default/101.jpg | Height: 224 Width: 224 | default | | ./train/default/102.jpg | Height: 224 Width: 224 | default | | ./train/default/103.jpg | Height: 224 Width: 224 | default | | ./train/default/104.jpg | Height: 224 Width: 224 | default | | ./train/default/105.jpg | Height: 224 Width: 224 | default | | ./train/default/106.jpg | Height: 224 Width: 224 | default | | ./train/default/107.jpg | Height: 224 Width: 224 | default | +-------------------------+------------------------+---------+ [2028 rows x 3 columns] Note: Only the head of the SFrame is printed. You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns. ``` ### Making the Model ```python import turicreate as tc # Load the data data = tc.SFrame('fire-smoke.sframe') # Make a train-test split train_data, test_data = data.random_split(0.8) # Create the model model = tc.image_classifier.create(train_data, target='label') # Save predictions to an SArray predictions = model.predict(test_data) # Evaluate the model and print the results metrics = model.evaluate(test_data) print(metrics['accuracy']) # Save the model for later use in Turi Create model.save('fire-smoke.model') # Export for use in Core ML model.export_coreml('fire-smoke.mlmodel') ``` \ ```Termcap Performing feature extraction on resized images... Completed 64/1633 Completed 128/1633 Completed 192/1633 Completed 256/1633 Completed 320/1633 Completed 384/1633 Completed 448/1633 Completed 512/1633 Completed 576/1633 Completed 640/1633 Completed 704/1633 Completed 768/1633 Completed 832/1633 Completed 896/1633 Completed 960/1633 Completed 1024/1633 Completed 1088/1633 Completed 1152/1633 Completed 1216/1633 Completed 1280/1633 Completed 1344/1633 Completed 1408/1633 Completed 1472/1633 Completed 1536/1633 Completed 1600/1633 Completed 1633/1633 PROGRESS: Creating a validation set from 5 percent of training data. This may take a while. You can set ``validation_set=None`` to disable validation tracking. Logistic regression: -------------------------------------------------------- Number of examples : 1551 Number of classes : 3 Number of feature columns : 1 Number of unpacked features : 2048 Number of coefficients : 4098 Starting L-BFGS -------------------------------------------------------- +-----------+----------+-----------+--------------+-------------------+---------------------+ | Iteration | Passes | Step size | Elapsed Time | Training Accuracy | Validation Accuracy | +-----------+----------+-----------+--------------+-------------------+---------------------+ | 0 | 6 | 0.018611 | 0.891830 | 0.553836 | 0.560976 | | 1 | 10 | 0.390832 | 1.622383 | 0.744681 | 0.792683 | | 2 | 11 | 0.488541 | 1.943987 | 0.733075 | 0.804878 | | 3 | 14 | 2.442703 | 2.512545 | 0.727917 | 0.841463 | | 4 | 15 | 2.442703 | 2.826964 | 0.861380 | 0.853659 | | 9 | 28 | 2.340435 | 5.492035 | 0.941328 | 0.975610 | +-----------+----------+-----------+--------------+-------------------+---------------------+ Performing feature extraction on resized images... Completed 64/395 Completed 128/395 Completed 192/395 Completed 256/395 Completed 320/395 Completed 384/395 Completed 395/395 0.9316455696202531 ``` We just got an accuracy of 94% on Training Data and 97% on Validation Data!