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Sam Vit Tiny Random



The sam-vit-tiny-random model is a neural network model designed for image classification tasks. It is based on the Vision Transformer (ViT) architecture, which uses self-attention mechanisms to process images. The model is trained using a random initialization strategy, where the weights of the neural network are randomly initialized, allowing the model to learn from scratch. This model can be used to classify images into different categories based on their visual content.

Use cases

The sam-vit-tiny-random model can be applied in a variety of use cases for image classification tasks. It can be used in the field of autonomous vehicles, where it can help classify objects on the road to aid in navigation and safety. In the healthcare industry, the model can be used for medical image analysis, such as identifying tumors or anomalies in radiology images. It can also be applied in the field of agriculture, where it can assist in the classification of crops or plant diseases for better crop management. Other potential use cases include surveillance systems, retail product recognition, and quality control in manufacturing. With its ability to classify images based on visual content, this model opens up opportunities for products and applications such as image search engines, recommendation systems, and augmented reality experiences.


Cost per run
Avg run time

Creator Models

Tiny Random Longformer$?35
Tiny Random Longformer Onnxtrue$?28
T5 Small Onnx$?4
Broken Onnx As Strided$?0
Netron Inspect Topmost Initializers$?0

Similar Models

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Try it!

You can use this area to play around with demo applications that incorporate the Sam Vit Tiny Random model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.

Currently, there are no demos available for this model.


Summary of this model and related resources.

Model NameSam Vit Tiny Random
Platform did not provide a description for this model.
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

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How much does it cost to run this model? How long, on average, does it take to complete a run?

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