Build an AWS Machine Learning Pipeline for Object Detection

Create an IAM user in AWS, avoid root access, balance programmatic and console access, apply least privilege, and securely manage administrator permissions, access keys, and login credentials.

Explain that AWS S3 is object storage, not a traditional database; it stores data for data lakes and cloud-native apps, and shows how Athena can query partitions for database-like use.

Explore pre-labeled data from open images v7 for object detection, including plastic bag labels, and download via Colab using voxel 51 before uploading to AWS SageMaker.

Learn how to prepare and manage object detection datasets for an AWS machine learning pipeline, including organizing train and validation folders, examining labels, and creating zipped archives for efficient upload.

discover how to locate the generated dataset in google colab, identify the train and validation image zips under content/route/51, and download the files for object detection.

Download and verify train images.zip and validation images.zip, then upload all dataset files to AWS to begin the object detection workflow.

Learn to create a SageMaker domain in Studio, set up an execution role, and prepare data by uploading to a SageMaker notebook before moving to S3 and model training.

Launch and configure a SageMaker Studio notebook for an object detection workflow, including navigating domains and initiating first-time Studio setup. Learn about Studio's collaborative space and real-time notebook editing.

Switch to the JupyterLab space and learn to set up a SageMaker space with mlt3 medium and distribution 2.2.1, featuring PyTorch and Transformers, and note differences from Studio Classic.

Learn how to create and configure a SageMaker notebook, choose a Docker image and kernel, and stop or restart notebooks to manage running costs.

Restart the SageMaker Studio notebook kernel, rename the notebook to object detection, and save your work before uploading code and data in the next steps.

Upload Open Images dataset v7 into SageMaker Studio's EFS via drag-and-drop or upload, then unzip train images and validation images into unzipped folders using Python.

Delete unused train and validation zip files after extracting images to save storage costs and shut down running SageMaker instances to avoid charges before exploratory data analysis.

Load and explore large train and validation csvs for object detection, inspect bounding boxes and label names, and filter to the plastic bag class using pandas on SageMaker.

Count the number of training and validation images and extract their IDs using glob-based path patterns. Verify that the IDs correspond to files in each folder for a csv-free workflow.

Parse the classes.csv to map class identifiers to names, load train and validation metadata, and filter for the plastic bag object to retrieve corresponding identifiers for object detection.

Explore how annotations structure image data for object detection by interpreting image IDs, label names, and normalized bounding boxes, including multiple boxes per image and visualization plans.

Visualize random images from train or validation sets with a Python function, using glob, NumPy, PIL, and Matplotlib. Load, index, and display a random image for dataset inspection.

Visualize random images with matplotlib, understand how invalid input parameter triggers exceptions, and compare train and validation contexts across notebook environments like Jupyter, SageMaker, and Colab.

Visualize many images at once to inspect datasets using a matplotlib grid of subplots, adjustable rows and columns, and train or validation image paths for efficient data exploration.

Use csv data to draw bounding boxes around images for object detection, specifying image id and the plastic bag label, then visualize and prepare to automate bounding box drawing.

Visualize bounding boxes by converting normalized x min and x max, y min and y max to pixel coordinates using image width and height, then draw rectangles with matplotlib patches.

Visualize random images with bounding boxes from train and validation datasets by looping over data frame rows to draw each box using x_min, x_max, y_min, and y_max.

Visualize multiple bounding boxes on random images by looping through bounding box data, converting normalized coordinates to pixel values using image width and height, and drawing red rectangles.

Clean and filter the train and validation frames to keep only the plastic bag label, yielding 517 train images, 986 bounding boxes, and 9 validation images with 9 bounding boxes.

Split the df_train_cleansed into 788 train and 198 test rows (80% training, 20% testing) with train_test_split and random_state 42; note the validation set isn't used for training.

Create a test images folder and its data subfolder, then move train images matching test IDs into the test set using code that builds directories and processes IDs.

Explore how to split image IDs for object detection by moving or copying images between train and test folders based on bounding box annotations and duplicates across dataframes.

Move or copy images from train to test folders based on IDs, using glob and path operations. Copy when an ID appears in both train and test, otherwise move.

Count train and test images with glob paths to verify correct placement, revealing 162 test images and 449 train images. Identify 94 duplicates where bounding boxes appear in both folders.

Verify train and test image directories have matching IDs, remove duplicates with sets, and use intersections to ensure IDs align with the data frame.

Verify that train and test image IDs align with their data frames by computing the intersection, correcting a slicing mistake, and confirming all IDs are present in both sets.

Convert train and test data frames into LST files and then MXNet record IO for an AWS SageMaker object detection workflow, using ResNet for CNN performance.

Learn how to convert a data frame to lst and recordio formats for mxnet object detection, detailing image index, class ids, bounding boxes, headers, and image paths.

Rename the label name to class name, copy dataframes to avoid warnings, add header and label width, and construct image path columns to prepare train and test data for RecordIO format.

Drop unnecessary columns and retain the required ones in the correct order, set class names to 0.000 for train and test frames, and prepare dataframes for recordio object detection.

Move images to the correct image path using shuttle.move, automatically creating missing directories, and verify train and test image folders contain the expected images.

Learn how to create production-ready LST files from object detection data by consolidating multiple bounding boxes per image into one line, following MXNet conventions for train and test.

Explore data augmentation by flipping images across the y axis, redefining bounding box coordinates, and updating train and test data frames to double the dataset.

Augment a random image by transposing and flipping left-right, recalculate the x coordinates for the bounding box, and visualize the transposed image with updated bounds.

Logically reorganize multi class object detection data by moving images into a class-based folder structure, update paths and frames, and prepare to visualize and recalculate bounding boxes after transposing images.

visualize random images with bounding boxes for plastic bag detection by loading train or test paths and using data frames to map image ids to bounding boxes.

Learn how to adjust bounding boxes when augmenting images, scaling normalized coordinates to the image size and flipping along the y axis, updating x_min and x_max accordingly.

Define a data augmentation function that flips images along the y axis, saves augmented images, and updates bounding box coordinates in the train or test data frames for object detection.

In an AWS machine learning pipeline for object detection, check train and test image counts before running the function to verify data augmentation and final data frame lengths.

Run the augment data function on the test set to create a test LST file. Verify augmented bounding boxes and flipped image paths for the test data.

Part 1 of augmenting the train dataset uses augment_data with the train parameter to create train.lst, verifying bounding boxes are 0 and 1 and including original and flipped images.

Augment the train dataset by flipping images along the y axis and verify new bounding box coordinates are saved correctly before starting training.

Verify that the data augmentation process correctly updates bounding boxes after flipping images by recomputing coordinates with image width and height, then prepare data for record IO format and training.

Learn how to manage AWS service quotas to access GPUs for training and hyperparameter tuning in SageMaker, including how to request quota increases for training jobs, notebook instances, and endpoints.

Install essential dependencies for the MXNet-based object detection pipeline, including apt-get updates, OpenCV, and MXNet; understand choosing ResNet or VGG architectures and future hyperparameter tuning.

Use mxnet im2rec.py from the Apache mxnet GitHub to convert test.lst and train.lst to record IO files (.rec).

Upload recordio files to a new s3 bucket, configure a sagemaker session and execution role, and manage train and validation channels with rec and lst files, including cleanup notes.

Configure the output location in S3 and download the AWS object detection algorithm from ECR, then set region, framework, and version to prepare for model training in SageMaker.

Set up a SageMaker estimator for object detection by configuring the training image, role, and a GPU instance, then define volume size, max run, and input mode.

Set up hyperparameters for an object detection model using transfer learning with pretrained Resnet 50, defining classes, epochs, learning rate schedule, momentum, weight decay, NMS-threshold.

Set up hyperparameter tuning in SageMaker by using Bayesian search to train multiple models, exploring learning rate, mini batch size, and optimizer options like SGD and Adam for object detection.

Configure hyperparameter tuning for an AWS SageMaker object detection training by setting max jobs and max parallel jobs, balancing model quality and cost with Bayesian search.

Learn to configure SageMaker with train and validation data paths in S3, set data channels, choose fully replicated distribution, and launch a hyperparameter tuning job using tuner.fit with epoch adjustments.

Configure an AWS SageMaker object-detection training job, adjust hyperparameters with bayesian tuning, fix learning rate scheduler to factor, and monitor progress via logs and metrics.

Monitor multiple training jobs for object detection, compare validation map scores, and tune SGD, batch size 16, and learning rate 0.0179 to identify the best model.

Review eight completed training jobs to identify the best object detection model with a 29% map score, analyzing hyperparameters like SGD, mini batch size, and learning rate in SageMaker.

Deploys a local SageMaker object detection endpoint in a notebook using a training image from ECR and the S3 model artifact.

Learn to deploy an AWS machine learning pipeline for object detection and visualize inferences by building a visualization function that draws bounding boxes on images using SageMaker endpoints.

Create a show plastic prediction function to call the deployed endpoint, convert images to byte arrays, and visualize detections with bounding boxes for manual validation before batch inference.

Test and verify the deployed endpoint by running predictions on validation images, correcting data issues, and highlighting how transfer learning enables accurate plastic bag detection with limited data.

Test the object-detection model on internet images using a ResNet-50 backbone trained on 800 images (400 real, 400 augmented) and tune the threshold to 25–30%; manage endpoints to control costs.

Build a batch transformation job locally in SageMaker notebook, then implement an AWS Step Functions pipeline to produce daily object detection inferences, including bounding boxes and classes, saved to S3.

Kick off the batch transform job by preparing sample data in the S3 bucket under images/year/month/day, and test with 4–5 images from free stock sites.

Review your batch transformation job in AWS SageMaker, locate batch output files in S3, and extract object detection results such as confidence and class for visualization of bounding boxes.

Visualize batch transformation results by extracting bounding boxes, confidence scores, and class IDs from .out JSON files and drawing boxes on corresponding images in S3 and SageMaker batch tests.