6 minute readCreated on January 16, 2020Last modified on June 1, 2020
Tagged with: 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
import os
from google.colab import drive
drive.mount('/content/drive')
Downloading Dataset from Kaggle
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
!mkdir default smoke fire
!ls data/data/img_data/train/default/*.jpg
img_1002.jpg img_20.jpg img_519.jpg img_604.jpg img_80.jpg
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The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate
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
!mkdir train
!mv default ./train
!mv smoke ./train
!mv fire ./train
Making the Image Classifier
Making an SFrame
!pip install turicreate
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')
+-------------------------+------------------------+
| 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
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')
Performing feature extraction on resized images...
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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...
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0.9316455696202531
We just got an accuracy of 94% on Training Data and 97% on Validation Data!