readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  datasets:
  - tarshnet
  widget:
  - src: https://www.estal.com/FitxersWeb/331958/estal_carroussel_wg_spirits_5.jpg
    example_title: Glass
  - src: https://origamijapan.net/wp-content/uploads/2013/10/2_600-1.jpg
    example_title: Paper
  - src: https://i0.wp.com/makezine.com/wp-content/uploads/2016/03/AdobeStock_79098618METAL.jpeg?ssl=1
    example_title: Metal
  ---
  
  # Vision Transformer (base-sized model) 
  
  Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. 
  
  Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
  
  ## Dataset
  
  The dataset used consist of  spans six classes: glass, paper, cardboard, plastic, metal, and trash. Currently, the dataset consists of 2527 images:
  
  * 501 glass
  * 594 paper
  * 403 cardboard
  * 482 plastic
  * 410 metal
  * 137 trash
  ## Fine_tuned Notebook
  
  This notebook outlines the steps from preparing the data in the Vit-acceptable format to training the model [Notebook](https://colab.research.google.com/drive/1RbmRPJ9bFLA_qK9RGgPoHZRnUTy_md5O?usp=sharing)
  
  ### How to use
  
  Just copy this lines below: 
  
  ```python
  from transformers import AutoFeatureExtractor, AutoModelForImageClassification
  from PIL import Image
  import requests
  url = 'https://www.estal.com/FitxersWeb/331958/estal_carroussel_wg_spirits_5.jpg'
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = AutoFeatureExtractor.from_pretrained("Aalaa/Fine_tuned_Vit_trash_classification")
  model = AutoModelForImageClassification.from_pretrained("Aalaa/Fine_tuned_Vit_trash_classification")
  
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits
  
  predicted_class_idx = logits.argmax(-1).item()
  print("Predicted class:", model.config.id2label[predicted_class_idx])
  ```
  
  For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).
  