Project developed for AWS Machine Learning Engineer Scholarship offered by Udacity (2023)
In recent years, deep learning has revolutionized the field of computer vision with its ability to accurately classify images. One of the most popular techniques for image classification is using convolutional neural networks (CNNs), which have shown excellent results in comparison with others approaches such as full connected neural networks. However, training these models from scratch can be computationally intensive and time-consuming. To overcome this, another approach called transfer learning has been used and has become increasingly popular.
This project, uses Amazon Web Services (AWS) SageMaker and Tensorflow to fine-tune a pretrained model for binary image classification. The dataset used in this project can be found at https://www.kaggle.com/datasets/deepcontractor/is-that-santa-image-classification. In addition, SageMaker Debugger was used to measure performance of training job, system resource usage, and for framework metrics analysis.
Note: model was trained and tested by using custom training and inference scripts.
- AWS SageMaker
- AWS SageMaker Debugger
- Tensorflow Framework version 2.9
- AWS S3
- SageMaker Hyperparameter Tuning
- SageMaker Endpoints
- SageMaker Profiler
The IS THAT SANTA? (Image Classification) dataset consists of 1230 images of Santa Claus and random images. This dataset is structured as follows:
For more information see: IS THAT SANTA? (Image Classification)
Notebook enviroment (Kernel)
Image: Tensorflow 2.10.0 Python 3.9 CPU optimized
Instance type: ml.t3.medium
Before we can access and download the Kaggle dataset, it is necessary to have a Kaggle account and a Kaggle API token (https://www.kaggle.com/account). Next paste the kaggle.json file in AWS SageMaker Studio as follows and execute the code snipped below to move file to root:
Move file to project root
mkdir ~/.kaggle
cp kaggle.json ~/.kaggle/
chmod 600 ~/.kaggle/kaggle.json
The code bellow shows how to donwload dataset and unzip file
kaggle datasets download -d deepcontractor/is-that-santa-image-classification
unzip is-that-santa-image-classification.zip
As Tensorflow does not support jpg we need to convert images from jpg to jpeg. For this step, we can use a bash script that uses ImageMagick (https://imagemagick.org/index.php).
To install ImageMagick run the following code developed by ARolek (https://gist.github.com/ARolek/9199329) on terminal:
Download the most recent package
wget http://www.imagemagick.org/download/ImageMagick.tar.gz
Uncompress the package
tar -vxf ImageMagick.tar.gz
Install the devel packages for png, jpg, tiff. these are dependencies of ImageMagick
sudo yum -y install libpng-devel libjpeg-devel libtiff-devel
Configure ImageMagick without X11. this is a server without a display (headless) so we don't need X11
cd ImageMagick
./configure --without-x
make && make install
Now we can use a bash script on terminal to convert images:
./convert_jpg_to_jpeg.sh -d -r is_that_santa/
Note: the dataset name was manually renamed to is-that-santa. The -d flag in convert_jpg_jpeg.sh stands for delete the orinal images and the -r for recursively converts images.
Now we can upload files to AWS s3:
aws s3 cp is_that_santa s3://{bucke-name}/datasets/ --recursive > /dev/null
Note: replace the {bucket-name} with your own bucket name. --recursive > /dev/null is optinal.
Requirements
tensorflow==2.10.1
smdebug==1.0.12
kaggle==1.5.12
Install
pip install -r requirements.txt
Or with MakeFile make command
make install
Debugger and Profiler outputs
For More details see: https://github.com/mathewsrc/Fine-Tuning-Pretrained-Image-Classification-Model-with-AWS-SageMaker-and-TensorFlow/blob/master/train_and_deploy.ipynb
https://www.tensorflow.org/tutorials/images/transfer_learning
https://www.tensorflow.org/tutorials/quickstart/advanced
https://docs.aws.amazon.com/sagemaker/latest/dg/model-access-training-data.html
https://github.com/aws/amazon-sagemaker-examples/blob/main/hyperparameter_tuning/
https://github.com/awslabs/sagemaker-debugger/blob/master/docs/tensorflow.md
https://github.com/aws/sagemaker-python-sdk/blob/master/doc/amazon_sagemaker_debugger.rst
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html
https://github.com/awslabs/sagemaker-debugger/blob/master/docs/api.md#tensorflow-specific-hook-api
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-enable-tensorboard-summaries.html
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-analyze-data.html
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-access-data-profiling-default-plot.html
https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/deploying_tensorflow_serving.html