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README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- pytorch
+- pet-breeds
+- oxford-iiit-pet-dataset
+- swin-transformer
+widget:
+  # You might need to configure a Space for the widget to work effectively
+  # Or provide a link to your Gradio Space if you create one
+  - example_url: "https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats-300.jpg"
+---
+# KEDU Breed Classifier (swin_tiny_patch4_window7_224)
+
+This model classifies pet breeds based on the Oxford-IIIT Pet Dataset.
+Model architecture: `swin_tiny_patch4_window7_224`.
+
+## How to Use
+
+1.  **Install dependencies:**
+    ```bash
+    pip install torch torchvision timm pytorch-lightning albumentations opencv-python-headless scikit-learn Pillow huggingface_hub python-box PyYAML
+    ```
+
+2.  **Download `inference.py`, `pytorch_model.ckpt`, and `label_encoder.pkl` from this repository.**
+
+3.  **Run inference:**
+    ```python
+    from inference import load_model_from_hf, load_label_encoder_from_hf, predict_breed
+
+    model = load_model_from_hf(repo_id="Hajorda/keduClassifier")
+    label_encoder = load_label_encoder_from_hf(repo_id="Hajorda/keduClassifier")
+
+    image_path = "path/to/your/pet_image.jpg" # Replace with your image path
+    predicted_breed, confidence = predict_breed(image_path, model, label_encoder)
+    print(f"Predicted: {predicted_breed}, Confidence: {confidence:.4f}")
+    ```
+
+## Model Details
+- **Dataset:** Oxford-IIIT Pet Dataset
+- **Number of Classes:** 37 
+- **Image Size:** (224, 224)
+
+## Performance
+- **Validation Accuracy:** 93.40%
+  (Note: This is the validation accuracy from the training run.)
+
+## Author
+Hajorda