Update README.md

README.md

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----
-language:
-- en
-metrics:
-- accuracy
-- precision
-- recall
-base_model:
-- facebook/deit-base-patch16-224
-pipeline_tag: image-classification
----
+# DeiT-Classification-Apparel 🏷️👕
+_A Deep Learning Model for Apparel Image Classification using DeiT_
+
+## 📝 Model Overview
+The **DeiT-Classification-Apparel** model is a **Vision Transformer (DeiT)** trained to classify different types of apparel. It leverages **Data-efficient Image Transformers (DeiT)** for improved image recognition with minimal computational resources.
+
+- **Architecture**: Vision Transformer (DeiT)
+- **Use Case**: Apparel classification
+- **Framework**: PyTorch
+- **Model Size**: 343MB
+- **Files**:
+  - `DeiT_Model_Parameter.pth` – Trained model weights
+  - `label_encoder.pkl` – Label encoder for class mapping
+
+## 📂 Files and Usage
+
+### 1️⃣ Load the Model
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+import pickle
+
+# Load Model
+model = torch.load_state_dict(torch.load("DeiT_Model_Parameter.pth", map_location=device))
+model.eval()
+
+# Load Label Encoder
+with open("label_encoder.pkl", "rb") as f:
+    label_encoder = pickle.load(f)
+```
+
+### 2️⃣ Perform Inference
+```python
+def predict(image_path):
+    # Load and preprocess image
+    image = Image.open(image_path).convert("RGB")
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+    input_tensor = transform(image).unsqueeze(0)
+
+    # Make prediction
+    with torch.no_grad():
+        output = model(input_tensor)
+        predicted_label = output.argmax(1).item()
+    
+    return label_encoder.inverse_transform([predicted_label])[0]
+
+# Example Usage
+image_path = "sample.jpg"
+prediction = predict(image_path)
+print(f"Predicted Apparel: {prediction}")
+```
+
+## 📌 Applications
+✅ Fashion e-commerce product categorization  
+✅ Retail inventory management  
+✅ Virtual try-on solutions  
+✅ Automated fashion recommendation  
+
+## 🛠️ Training Details
+- **Dataset**: Custom apparel dataset
+- **Optimizer**: Adam
+- **Loss Function**: CrossEntropyLoss
+- **Hardware Used**: NVIDIA T4 GPU
+
+## 📢 Citation
+If you use this model, please cite:
+```
+@misc{bobs24_deit_classification_2024,
+  author = {bobs24},
+  title = {DeiT-Classification-Apparel},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/bobs24/DeiT-Classification-Apparel}}
+}