A promising solution for a Computer Vision problem with the power to combine state-of-the-art technologies: Deep Learning with Apache Spark. We will leverage the power of Deep Learning Pipelines for a Multi-Class image classification problem.
Deep Learning Pipelines is a high-level Deep Learning framework that facilitates common Deep Learning workflows via the Spark MLlib Pipelines API. It currently supports TensorFlow and Keras with the TensorFlow-backend. The library comes from Databricks.
Blog Articles: Read technical description from below sources.
Installation is bit pesky right now. So, I decided to write it separately. Check this GitHub-Gist to install necessary packages and technical stuffs.
We choose NumtaDB as a source of our datasets. It's a collection of Bengali Handwritten Digit data. The dataset contains more than 85,000 digits from over 2,700 contributors. But here we're not planning to work on the whole data set rather than choose randomly 50 images of each class. You can get the sample of some images from here. It Contains 500 in total.
Here we combine the InceptionV3 model and logistic regression in Spark. The DeepImageFeaturizer automatically peels off the last layer of a pre-trained neural network and uses the output from all the previous layers as features for the logistic regression algorithm.
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.classification import LogisticRegression
from pyspark.ml import Pipeline
from sparkdl import DeepImageFeaturizer
# model: InceptionV3
# extracting feature from images
featurizer = DeepImageFeaturizer(inputCol="image",
outputCol="features",
modelName="InceptionV3")
# used as a multi class classifier
lr = LogisticRegression(maxIter=5, regParam=0.03,
elasticNetParam=0.5, labelCol="label")
# define a pipeline model
sparkdn = Pipeline(stages=[featurizer, lr])
spark_model = sparkdn.fit(train) # start fitting or trainingevaluation matrix
F1-Score 0.81117
Precision 0.84220
Recall 0.80909
Accuracy 0.80909
confusion Metrix
classification Report
precision recall f1-score support
Class 0 1.00 0.92 0.96 13
Class 1 0.57 1.00 0.73 8
Class 2 0.64 1.00 0.78 7
Class 3 0.88 0.70 0.78 10
Class 4 0.90 1.00 0.95 9
Class 5 0.67 0.83 0.74 12
Class 6 0.83 0.62 0.71 8
Class 7 1.00 0.80 0.89 10
Class 8 1.00 0.80 0.89 20
Class 9 0.70 0.54 0.61 13
micro avg 0.81 0.81 0.81 110
macro avg 0.82 0.82 0.80 110
weighted avg 0.84 0.81 0.81 110
Predicted Samples
['image', 'label', 'features', 'rawPrediction', 'probability', 'prediction']
+--------------------+----------+-----+
| image|prediction|label|
+--------------------+----------+-----+
|[file:/home/i...| 1.0| 1|
|[file:/home/i...| 8.0| 8|
|[file:/home/i...| 9.0| 9|
|[file:/home/i...| 1.0| 8|
|[file:/home/i...| 1.0| 1|
|[file:/home/i...| 1.0| 9|
|[file:/home/i...| 0.0| 0|
|[file:/home/i...| 2.0| 9|
|[file:/home/i...| 8.0| 8|
|[file:/home/i...| 9.0| 9|
|[file:/home/i...| 0.0| 0|
|[file:/home/i...| 4.0| 0|
|[file:/home/i...| 5.0| 9|
|[file:/home/i...| 1.0| 1|
|[file:/home/i...| 9.0| 9|
|[file:/home/i...| 9.0| 9|
|[file:/home/i...| 1.0| 1|
|[file:/home/i...| 1.0| 1|
|[file:/home/i...| 9.0| 9|
|[file:/home/i...| 3.0| 6|
+--------------------+----------+-----+
only showing top 20 rows
Related Technologies
- Distributed DL with Keras & PySpark — Elephas
- Distributed Deep Learning Library for Apache Spark — BigDL
- TensorFlow to Apache Spark clusters — TensorFlowOnSpark
References
- Databricks: Deep Learning Guide
- Apache Spark: PySpark Machine Learning