vit small patch16 224 LLM Model

Introduction

The vit-small-patch16-224 model is designed for image classification tasks. It is a Vision Transformer (ViT) model focusing on vision-related applications using datasets like ImageNet. The model is implemented in PyTorch and is compatible with SafeTensors.

Architecture

This model is a Vision Transformer (ViT) version, specifically the "small" variant with patch size 16 and input resolution of 224x224 pixels. It was converted from the timm repository to Hugging Face format to enable easier application and deployment.

Training

The model was trained on the ImageNet dataset. It is structured to use the same approach as the ViT-base model, making it straightforward for users familiar with other ViT architectures. The model requires a PyTorch environment, particularly torch version 2.0 or higher for SafeTensors compatibility.

Guide: Running Locally

Setup Environment: Ensure you have Python and PyTorch installed. For SafeTensors, torch 2.0 is required.
Install Dependencies: Use pip to install necessary libraries:
```
pip install torch torchvision transformers safetensors
```

Load the Model:

from transformers import ViTFeatureExtractor, ViTModel
feature_extractor = ViTFeatureExtractor.from_pretrained('WinKawaks/vit-small-patch16-224')
model = ViTModel.from_pretrained('WinKawaks/vit-small-patch16-224')

Inference: Use the feature extractor and model for inference on images.
Cloud GPUs: Consider using cloud services such as AWS, GCP, or Azure for GPU access to enhance processing speed.

License

The vit-small-patch16-224 model is licensed under the Apache 2.0 License. This allows for both personal and commercial use, with conditions for attribution and distribution.

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