Update README.md

README.md

@@ -17,6 +17,15 @@ tags:
 
 This model is a fine-tuned Vision Transformer (ViT) for document layout classification based on the DocLayNet dataset.
 
+Trained on images of the document categories from DocLayNet dataset where the categories namely(with their indexes) are :
+
+{'financial_reports': 0,
+ 'government_tenders': 1,
+ 'laws_and_regulations': 2,
+ 'manuals': 3,
+ 'patents': 4,
+ 'scientific_articles': 5} 
+
 ## Model description
 
 This model is built upon the `google/vit-base-patch16-224-in21k` Vision Transformer architecture and fine-tuned specifically for document layout classification. The base ViT model uses a patch size of 16x16 pixels and was pre-trained on ImageNet-21k. The model has been optimized to recognize and classify different types of document layouts from the DocLayNet dataset.
@@ -44,9 +53,27 @@ The training was made with following hyperparameters:
     'optimizer': 'AdamW'
 }
 
+```
 
 ## Evaluation results
 The model achieved the following performance metrics on the test set:
 
 Test Loss: 0.8622
-Test Accuracy: 81.36%
+Test Accuracy: 81.36%
+
+
+
+## Usage
+
+
+```python
+from transformers import pipeline
+
+# Load the model using the image-classification pipeline
+pipe = pipeline("image-classification", model="kaixkhazaki/vit_doclaynet_base")
+
+# Test it with an image
+result = pipe("path_to_image.jpg")
+print(result)
+
+```