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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- image-classification |
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datasets: |
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- tarshnet |
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widget: |
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- src: https://www.estal.com/FitxersWeb/331958/estal_carroussel_wg_spirits_5.jpg |
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example_title: Glass |
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- src: https://origamijapan.net/wp-content/uploads/2013/10/2_600-1.jpg |
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example_title: Paper |
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- src: https://i0.wp.com/makezine.com/wp-content/uploads/2016/03/AdobeStock_79098618METAL.jpeg?ssl=1 |
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example_title: Metal |
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--- |
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# Vision Transformer (base-sized model) |
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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. |
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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. |
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## Dataset |
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The dataset used consist of spans six classes: glass, paper, cardboard, plastic, metal, and trash. Currently, the dataset consists of 2527 images: |
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* 501 glass |
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* 594 paper |
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* 403 cardboard |
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* 482 plastic |
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* 410 metal |
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* 137 trash |
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## Fine_tuned Notebook |
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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) |
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### How to use |
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Just copy this lines below: |
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```python |
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification |
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from PIL import Image |
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import requests |
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url = 'https://www.estal.com/FitxersWeb/331958/estal_carroussel_wg_spirits_5.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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feature_extractor = AutoFeatureExtractor.from_pretrained("Aalaa/Fine_tuned_Vit_trash_classification") |
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model = AutoModelForImageClassification.from_pretrained("Aalaa/Fine_tuned_Vit_trash_classification") |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#). |
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