Name: Amazon Rekognition Custom Labels VS Sagemaker Image Classification & Object Detection Algorithms
Rating: 4.7 (100 reviews)

Introducing Amazon Rekognition:

Amazon Rekognition makes it easy to add image and video analysis to your applications. You just provide an image or video to the Amazon Rekognition API, and the service can identify objects, people, text, scenes, and activities. It can detect any inappropriate content as well.

Introducing Amazon Sagemaker:

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models and then directly deploy them into a production-ready hosted environment.

Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected plants, or detect animated characters in videos.
With Amazon Rekognition Custom Labels, no machine learning expertise is required. You can easily train a model by providing custom data and get recommendations using it’s console. Everything you can do with just a few clicks.
With Amazon Rekognition Custom Labels, you can easily do image classification, object detection kind of model training along with custom image labelling part in just a few clicks.
Rekognition is an easy way to train a model for image classification and object detection by clicking a few buttons on the console.
Rekognition training and inference pricing for the Mumbai region are

While training a model, it could scale training resources according to the data size & complexity of a model. If it’s using 1 resource on training time, then the cost would be $1.00/hr, and if it uses 2 resources, then the cost would be 2*$1.00/hr.
Here we can’t exactly estimate the cost of training a model because training resources and it’s cost could be increased or decreased based on data size & model complexity.
Rekognition does not have the functionality of incremental training. If you want to train a model on new data, you have to train that model on entirely new and old data, which will act like a new model version.
Rekognition does not provide hyperparameter tunning, sagemaker clarify, and other features to improve model performance and model monitoring.

Image Classification:
- The Amazon SageMaker image classification algorithm is a supervised learning algorithm that supports multi-label classification. It takes an image as input and outputs one or more labels assigned to that image. It uses a convolutional neural network (ResNet) that can be trained from scratch or trained using transfer learning when a large number of training images are not available.
- You can train a model using the sagemaker image classification algorithm to classify images.
- Here you must be Machine Learning or Data Science expert to implement this algorithm using amazon sagemaker.
- Manually you have to perform task from data preprocessing to model training and inference.
- Here you can use hyperparameter tunning, sagemaker clarify and other feature to improve model performance and model monitoring.
- Image Classification has functionality to incremental training of a model and canary deployment.

Object Detection:
- The Amazon SageMaker Object Detection algorithm detects and classifies one or more objects in images using a single deep neural network. It is a supervised learning algorithm that takes images as input and identifies all instances of objects within the image scene. The object is categorized into one of the classes in a specified collection with a confidence score that belongs to the class. Its location and scale in the image are indicated by a rectangular bounding box.
- You can train a model using sagemaker object detection algorithm to identify objects from images.
- Here you must be Machine Learning or Data Science expert to implement this algorithm using amazon sagemaker.
- Manually you have to perform a task from data preprocessing to model training and inference.
- Here you can use hyperparameter tunning, sagemaker clarify, and other feature to improve model performance and model monitoring.
- Image Classification has the functionality of incremental training to a model and canary deployment.

Training Instance Price

Inference Instance Price

Conclusion:

Use Rekognition Custom Label If you want to easily train a model and classify image or object detection.
If you don’t care about model incremental training, cost and additional configuration, then you can use Rekognition Custom Label.
Use Sagemaker Image Classification or Object Detection if you want to classify an image or detect an object and If you have a machine learning or data science expert who can train a model by doing custom configuration and model monitoring.
Use Sagemaker Image Classification or Object Detection if model performance, incremental training, and cost savings are important and if you require additional configuration like sagemaker clarify, canary deployment, etc., to make it a strong model.
The Inference price of Rekognition is very high than the inference price of Sagemaker Instances.
Even GPU instance price for Sagemaker is high, but it won\’t take much time to train, but Rekognition will take much time to train, and it will charge a higher amount.