- Use cases
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1. Preprocessing
- SageMaker Object Detection preprocessing
- Rekognition Object Detection preprocessing
- SageMaker Kmeans preprocessing
- Autopilot preprocessing
- DeepAR preprocessing
- Personalize preprocessing
- Select, drop or extract Columns
- Split dataset to Train and Test
- Upload to s3
- Forecast preprocessing
- Rekognition Classification preprocessing
- SageMaker Image Classification preprocessing
- Xgboost preprocessing
- Blazingtext preprocessing
- Comprehend custom preprocessing
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2. Training
- SageMaker Object Detection training
- Rekognition Object Detection training
- Forecast training
- Personalize training
- BlazingText training
- DeepAR training
- SageMaker Kmeans training
- Comprehend custom training
- Autopilot Training
- Xgboost Training
- Autogluon training
- Rekognition Classification training
- SageMaker Image Classification training
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3. Inference
- SageMaker Object Detection inference
- Forecast inference
- Rekognition Object Detection inference
- Comprehend custom inference
- Personalize inference
- Autopilot Inference
- BlazingText Inference
- Custom SageMaker model Inference
- DeepAR Inference
- Rekognition Classification inference
- SageMaker Image Classification inference
- SageMaker Kmeans inference
- Xgboost Inference
- Contribute a use case or contact us for help.
- Frequently Asked Questions
Rekognition Classification preprocessing
Rekognition classification deals with learning image-level tags. To train your model, Amazon Rekognition Custom Labels require the images in a dataset to be labeled with information about the scenes or concepts in your images.
If your image represents a scene or concept, such as a wedding or sport, the image as a whole needs an identifying image-level label. An image needs at least one label. You can add others so that the model can detect different classes of information—for example, countryside or sky. In this step, you add image-level labels to an image.
If you are looking to identify objects within images, check out Rekognition object detection preprocessing
Upload data to S3
To make sure the training step is easy, organize the different classes of your data in different folders (prefixes) in S3. Suppose you have two classes of image-level labels (this could be rivers vs. oceans, outdoor vs. indoor, kitchen vs. living room etc.), upload these classes of images into two different folders. Names of these folders can match the class of images that it contains. Here, we just use class-1 and class-2 as sample names for the folders, inside a bucket called rekognitioncustomlabels. The class-1 folder only contains images that fall into the first class etc.
You can have up to 250 different folders (we suggest you start with 2 - 3) or image-level labels, with at least one image per label (we suggest you have at least 100 examples in each folder). The maximum number of images per dataset is 250,000. Make sure that the minimum image dimension of each image file 64 pixels x 64 pixels, and the maximum is 4096 pixels x 4096 pixels.
Other limits are specified here
Create a dataset
Navigate to Rekognition on the console and click “Amazon Rekognition”:
Click Use Custom Labels
On the left sidebar / menu, click datasets
Provide a dataset name and choose Import images from S3
Switch to the S3 console, copy and paste the bucket permissions into the bucket that contains your data:
Switch back to the Rekognition console, enter the S3 path, and select Automatic labeling, and click Submit
Related content:
- ☞ Autogluon training – 2 min read
- ☞ Rekognition Classification inference – 1 min read
- ☞ Rekognition Classification training – 1 min read