Update README.md

README.md

@@ -25,40 +25,25 @@ model-index:
       value: 1.0
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# vit-base-beans-demo-v5
-
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0155
-- Accuracy: 1.0
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0002
-- train_batch_size: 16
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 4
+# Fine-Tuned ViT for Beans Leaf Disease Classification
+
+## Model Information
+* **Model Name**: Fine-Tuned ViT (Vision Transformer)
+* **Base Model**: Google/ViT-base-patch16-224-in21k
+* **Task**: Image Classification (Beans Leaf Disease Classification)
+* **Dataset**: Beans leaf dataset with images of diseased and healthy leaves.
+
+## Problem Statement
+The goal of this model is to classify leaf images into three categories:
+```
+{
+  "angular_leaf_spot": 0,
+  "bean_rust": 1,
+  "healthy": 2,
+}
+```
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6338c06c107c4835a05699f9/3qwVfVNQSt0KHe8t_OCrT.png)
 
 ### Training results
 
@@ -67,10 +52,10 @@ The following hyperparameters were used during training:
 | 0.1495        | 1.54  | 100  | 0.0910          | 0.9774   |
 | 0.0121        | 3.08  | 200  | 0.0155          | 1.0      |
 
+## Framework versions
++ Transformers 4.33.2
++ Pytorch 2.0.1+cu118
++ Datasets 2.14.5
++ Tokenizers 0.13.3
 
-### Framework versions
 
-- Transformers 4.33.2
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.5
-- Tokenizers 0.13.3