Update README.md

README.md

@@ -6,10 +6,10 @@ tags:
 widget:
 - src: https://www.estal.com/FitxersWeb/331958/estal_carroussel_wg_spirits_5.jpg
   example_title: Glass
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
-  example_title: Teapot
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
-  example_title: Palace
+- 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) 
@@ -18,14 +18,6 @@ Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 2
 
 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.
 
-## Model description
-
-The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
-
-Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
-
-By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
-
 ## Dataset
 
 The dataset used consist of  spans six classes: glass, paper, cardboard, plastic, metal, and trash. Currently, the dataset consists of 2527 images: