vit tiny patch16 224

Introduction

The vit-tiny-patch16-224 model is an image classification model leveraging the Vision Transformer (ViT) architecture. It was converted from the timm repository and is designed to operate similarly to the ViT-base model.

Architecture

This model is part of the Vision Transformer (ViT) family and is tailored for image classification tasks. It handles input images with a 16x16 patch size and a 224x224 resolution.

Training

The model was trained using the ImageNet dataset. The weights were not originally published by Google for vit-tiny and vit-small models; instead, they were converted from the timm repository by the author.

Guide: Running Locally

1. Install Dependencies: Ensure you have PyTorch installed, preferably in a Torch 2.0 environment for safetensors compatibility.
2. Download Model: Obtain the model weights from Hugging Face.
3. Set Up Environment: Use Python to set up your environment and load the model.
4. Run Inference: Utilize the model for image classification tasks.
5. Cloud GPUs: Consider using cloud GPU providers such as AWS, Google Cloud, or Azure for running the model efficiently.

License

The vit-tiny-patch16-224 model is distributed under the Apache-2.0 license, allowing for broad use, modification, and distribution.