Update README.md

README.md

@@ -13,4 +13,60 @@ tags:
 - school
 metrics:
 - accuracy
----
+---
+# 🤖 ViT Emotion Classifier
+
+This is a lightweight [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit) model fine-tuned to classify **emotions** from facial images using a custom dataset of school-aged individuals. It supports 8 emotional categories and is designed to work well on small datasets and limited compute.
+
+---
+
+## 🧠 Supported Emotions
+
+The model predicts one of the following emotional states:
+
+| Label ID | Emotion            |
+|----------|--------------------|
+| 0        | anxious-fearful    |
+| 1        | bored              |
+| 2        | confused           |
+| 3        | discouraged        |
+| 4        | frustrated         |
+| 5        | neutral            |
+| 6        | positive           |
+| 7        | suprised           |
+
+---
+
+## 📦 Model Details
+
+- **Model Type**: `ViTForImageClassification`
+- **Backbone**: `vit-small-patch16-224`
+- **Dataset**: [`Dc-4nderson/feelings_classfication_dataset`](https://huggingface.co/datasets/Dc-4nderson/feelings_classfication_dataset)
+- **Framework**: PyTorch
+- **Labels**: 8 emotions (defined in `config.json`)
+- **Trained on**: Google Colab with < 600 images
+
+---
+
+## 🧪 Usage
+
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load model + processor
+processor = AutoImageProcessor.from_pretrained("Dc-4nderson/vit-emotion-classifier")
+model = AutoModelForImageClassification.from_pretrained("Dc-4nderson/vit-emotion-classifier")
+
+# Load image and preprocess
+image = Image.open("your_image.jpg").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    pred = torch.argmax(outputs.logits, dim=1).item()
+    label = model.config.id2label[str(pred)]
+
+print("🧠 Predicted Emotion:", label)