Update README.md

README.md

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 - bleu
 library_name: transformers
 pipeline_tag: image-classification
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+---
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+Model Overview:
+The Vision Transformer (ViT) is a transformer encoder model designed for image recognition tasks. It was pretrained on a large dataset of 14 million images and 21,843 classes known as ImageNet-21k, and fine-tuned on ImageNet 2012, which consists of 1 million images across 1,000 classes.
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+How It Works:
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+Input Representation: Images are split into fixed-size patches (16x16 pixels) and linearly embedded. A special [CLS] token is added at the beginning of the sequence to indicate the image's classification.
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+Transformer Encoder: The model uses a transformer encoder architecture, similar to BERT for text, to process the image patches. Absolute position embeddings are added to encode spatial information before inputting the sequence into transformer layers.
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+Classification: After processing through the transformer layers, the output from the [CLS] token is used for image classification. This token's final hidden state represents the entire image's features.
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+Intended Uses:
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+Image Classification: ViT can be directly used for image classification tasks. By adding a linear layer on top of the [CLS] token, the model can classify images into one of the 1,000 ImageNet classes.
+Limitations:
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+Resolution Dependency: While the model was fine-tuned on ImageNet at 224x224 resolution, better performance is achieved with higher resolutions such as 384x384. Larger models generally yield better results but require more computational resources.
+Training Details:
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+Preprocessing: Images are resized to 224x224 pixels and normalized across RGB channels.
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+Training: Pretraining was conducted on TPUv3 hardware with a batch size of 4096 and learning rate warmup. Gradient clipping was applied during training to enhance stability.
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+Evaluation Results:
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+Performance: Detailed evaluation results on various benchmarks can be found in tables from the original paper. Fine-tuning the model on higher resolutions typically improves classification accuracy.