Sold by: MphasisÂ
Deployed on AWS
This solution automatically identifies and trains the best performing deep learning model for image classification.
Overview
This solution will evaluate between several deep learning models of various architectures on the user provided data. It will identify the best performing deep learning model architecture on the basis of validation metric for image classification. This will reduce the time and effort for the model building task for a data scientist. This solution automates several of deep learning tasks in data science.
Highlights
- This solution will help identify the best deep learning model architecture for the image classification data set and help improve the turn-around time for any image processing development as well as save time of a data scientist.
- This solution can be used to solve image classification problems in various domains like banking , insurance , retail , legal and ecommerce.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $10.00 |
ml.m5.2xlarge Training Recommended | Algorithm training on the ml.m5.2xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes
Additional details
Inputs
- Summary
This algorithm takes a zip file as an input. This zip file to be uploaded for training the model should follow the following file structure Sample.zip |----class 1 |----|----img1.png |----class 2 |----|----img1.png |----… |----class n |----|----img1.png |----dict.json
- Input MIME type
- text/csv, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Image-Classifier-with-auto-Deep-Learning
https://github.com/Mphasis-ML-Marketplace/Image-Classifier-with-auto-Deep-Learning
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Train/test data | The zip file should contain images. The folder structure should be as specified in the usage instructions. Please go through them for more information. The current accepted image types are: png, jpg | Type: Continuous
Minimum: 0
Maximum: 255 | Yes |
max_try | Specifies the number of architecture the model searcher should check before identifying the best architecture | Type: Integer
Minimum: 1 | Yes |
no_epochs | Specifies the number of epochs each searched architecture should be evaluated for. | Type: Integer
Minimum: 1 | Yes |
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
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1 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Pawan R.
Automated Image Classifier using DL make image classification much easier.
Reviewed on Oct 12, 2023
Review provided by G2
What do you like best about the product?
When evaluating the advantages of using deep learning for image classifiers, important points such as ease of use and cost-effectiveness need to be taken into account. Additionally, on the professional side, Auto DL helps improve data transparency and user understanding, which is especially important when separating images from large files for training and testing purposes.
What do you dislike about the product?
Automated deep learning image classifiers sometimes create problems in terms of transparency, information and needs, overfitting, intelligence, bias, interpretation, and judgment. It is important to consider these limitations as well as their benefits and specific use requirements.
What problems is the product solving and how is that benefiting you?
Automatic deep learning image classifiers solve many problems. It makes content easier to manage, improves truth seeking, improves diagnosis, improves business management and increases driving safety. These applications provide great benefits to our businesses, individuals and communities by increasing efficiency and accuracy in many areas.