Update README.md

README.md

@@ -1,39 +1,24 @@
 ---
-license: apache-2.0
-pipeline_tag: image-classification
-base_model: google/vit-base-patch16-224
 tags:
 - image-classification
+- skin-disease
+- vision
 - vit
-- generated_from_trainer
-datasets:
+- pytorch
 - image_folder
-metrics:
-- accuracy
+pipeline_tag: image-classification
 widget:
-  - src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png
-    example_title: Upload a skin disease image
-model-index:
-- name: vit_base_patch16_224-finetuned-SkinDisease
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: image_folder
-      type: image_folder
-      config: default
-      split: train
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.9343
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/image_2.jpg
+  example_title: Skin Disease Example
+license: apache-2.0
+base_model:
+- google/vit-base-patch16-224
 ---
 
 # vit_base_patch16_224-finetuned-SkinDisease
 
-This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the `image_folder` dataset.  
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on a custom skin disease dataset (`image_folder` format).  
 It achieves the following results on the evaluation set:
 
 - **Loss**: 0.1992  
@@ -48,35 +33,34 @@ It processes input images of size **224x224 pixels** and outputs the most likely
 
 ---
 
-## 🩺 Intended uses & limitations
+## 🔗 Intended uses & limitations
 
 - ✅ For clinical support, not for standalone medical diagnosis.  
 - ✅ Designed for educational, research, and proof-of-concept use.
 
 ---
 
-## 📊 Training and evaluation data
+## 🧪 Training and evaluation data
 
-- **Dataset used**: Custom image dataset with labeled skin diseases.  
-- **Preprocessing**: Resized to 224x224, normalized.
+- **Dataset used**: Custom image dataset with labeled skin diseases  
+- **Preprocessing**: Resized to 224×224, normalized
 
 ---
 
 ## ⚙️ Training procedure
 
-### Hyperparameters
-
-- **Learning Rate**: 5e-05  
-- **Epochs**: 10  
-- **Batch Size**: 32  
-- **Optimizer**: Adam  
-- **Scheduler**: Linear w/ warmup  
-- **Seed**: 42  
+**Hyperparameters**:
+- Learning Rate: 5e-05  
+- Epochs: 10  
+- Batch Size: 32  
+- Optimizer: Adam  
+- Scheduler: Linear with warmup  
+- Seed: 42  
 
-### Training results
+**Training results**:
 
 | Epoch | Val Loss | Accuracy |
-|:-----:|:--------:|:--------:|
+|-------|----------|----------|
 | 1     | 0.8248   | 0.7647   |
 | 2     | 0.4236   | 0.8748   |
 | 3     | 0.3154   | 0.9021   |
@@ -95,4 +79,4 @@ It processes input images of size **224x224 pixels** and outputs the most likely
 - `transformers`: 4.33.2  
 - `pytorch`: 2.0.0  
 - `datasets`: 2.1.0  
-- `tokenizers`: 0.13.3  
+- `tokenizers`: 0.13.3