Image classification on Custom Dataset Using FasterViT

Fast Vision Transformers with Hierarchical Attention

Learn to perform Image classification with custom dataset using FasterViT model.

GitHub: https://github.com/AarohiSingla/Faste...

Dataset added in GitHub repo: https://github.com/AarohiSingla/Faste...

Email: [email protected]

FasterViT

FasterViT, a fast vision transformer model developed by NVIDIA.
FasterViT (Faster Vision Transformer) is a variant of the Vision Transformer (ViT) architecture, designed to address some of the performance and efficiency challenges associated with traditional transformer models in image classification tasks.
Traditional Vision Transformers apply the transformer architecture, originally developed for natural language processing tasks, to image data. ViTs divide an image into patches, flatten them, and then process these patches as a sequence using a transformer model. While ViTs have shown promising results in image classification, they often require significant computational resources and have long inference times due to their complexity.
FasterViT is designed to be more computationally efficient than standard ViTs. This is achieved through architectural changes that reduce the number of parameters and floating-point operations (FLOPs) required for inference.
Sparse Attention Mechanisms: Incorporating sparse attention mechanisms can help reduce the computational load by focusing the model's attention on the most relevant parts of the input.


#computervision #transformers #nvidia #imageclassification