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authorNavan Chauhan <navanchauhan@gmail.com>2021-05-26 23:58:29 +0530
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+---
+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 <a href="/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/"> my previous post</a>*
+
+
+## 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!