# Built-in SageMaker AI Algorithms for Computer Vision
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SageMaker AI provides image processing algorithms that are used for image classification, object detection, and computer vision.
+ [Image Classification - MXNet](image-classification.md)—uses example data with answers (referred to as a *supervised algorithm*). Use this algorithm to classify images.
+ [Image Classification - TensorFlow](image-classification-tensorflow.md)—uses pretrained TensorFlow Hub models to fine-tune for specific tasks (referred to as a *supervised algorithm*). Use this algorithm to classify images.
+ [Object Detection - MXNet](object-detection.md)—detects and classifies 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.
+ [Object Detection - TensorFlow](object-detection-tensorflow.md)—detects bounding boxes and object labels in an image. It is a supervised learning algorithm that supports transfer learning with available pretrained TensorFlow models.
+ [Semantic Segmentation Algorithm](semantic-segmentation.md)—provides a fine-grained, pixel-level approach to developing computer vision applications.


| Algorithm name | Channel name | Training input mode | File type | Instance class | Parallelizable | 
| --- | --- | --- | --- | --- | --- | 
| Image Classification - MXNet | train and validation, (optionally) train\$1lst, validation\$1lst, and model | File or Pipe | recordIO or image files (.jpg or .png)  | GPU | Yes | 
| Image Classification - TensorFlow | training and validation | File | image files (.jpg, .jpeg, or .png)  | CPU or GPU | Yes (only across multiple GPUs on a single instance) | 
| Object Detection | train and validation, (optionally) train\$1annotation, validation\$1annotation, and model | File or Pipe | recordIO or image files (.jpg or .png)  | GPU | Yes | 
| Object Detection - TensorFlow | training and validation | File | image files (.jpg, .jpeg, or .png)  | GPU | Yes (only across multiple GPUs on a single instance) | 
| Semantic Segmentation | train and validation, train\$1annotation, validation\$1annotation, and (optionally) label\$1map and model | File or Pipe | Image files | GPU (single instance only) | No |