--- 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#).