Create README.md

README.md

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+---
+license: cc
+language:
+- en
+
+---
+
+# Model Card for AutoML Cuisine Classification
+
+This model card documents the **AutoML Cuisine Classification** model trained with AutoGluon Multimodal on a classmate’s dataset of food images.  
+The task is to predict whether a food image belongs to **Asian** or **Western** cuisine (binary classification).
+
+---
+
+## Model Details
+- **Developed by:** Bareethul Kader  
+- **Framework:** AutoGluon Multimodal  
+- **Repository:** bareethul/image-dataset-model  
+- **License:** CC BY 4.0  
+
+---
+
+## Intended Use
+### Direct Use
+- Educational demonstration of AutoML on an image classification task.  
+- Comparison of different backbones (ResNet18, MobileNetV3, EfficientNet-B0).  
+- Exploring effects of augmentation and model selection under constrained compute budget.  
+
+### Out of Scope Use
+- Not intended for production deployments in food classification systems.  
+- May not generalize to cuisines other than “Asian vs Western,” or to non-restaurant/home cooked settings.  
+- Not meant for health/dietary or allergy related automation.  
+
+---
+
+## Dataset
+- **Source:** [maryzhang/hw1-24679-image-dataset](https://huggingface.co/datasets/maryzhang/hw1-24679-image-dataset)  
+- **Task:** Binary image classification (label 0 = Western cuisine, label 1 = Asian cuisine)  
+- **Size:**  
+  - Original images: 40  
+  - Augmented images: 320  
+  - Total: ≈ 360 images  
+- **Features:**  
+  - `image`: Image (RGB, as provided)  
+  - `label`: Integer 0 or 1  
+
+---
+
+## Training Setup
+- **AutoML framework:** AutoGluon Multimodal (`MultiModalPredictor`)  
+- **Evaluation metric:** Accuracy  
+- **Budget:** ~600 seconds (10 minutes) for quick runs; longer (~1800s) for full run and more accuracy.
+- **Hardware:** Google Colab (GPU, typical environment)  
+- **Search Space:**  
+  - Backbones: `resnet18`, `mobilenetv3_small_100`, `efficientnet_b0`  
+- **Preprocessing / Augmentation:** As provided in dataset (augmented split); resize and standard image transforms as in dataset loading  
+
+---
+
+## Results
+- **Best model (AutoGluon selected):** *efficientnet_b0*  
+- **Validation Accuracy:** *0.96875*   
+
+---
+
+## Limitations, Biases, and Ethical Notes
+- Small dataset size -> overfitting risk.  
+- Augmented data may not capture all real world variance (lighting, background, etc.).  
+- Binary classification “Asian vs Western” is coarse; many cuisines and dishes don’t neatly fit.  
+- Labeling reflects simplified categories; cultural/geographic nuance lost.  
+
+---
+
+## Example Inference
+```python
+from autogluon.multimodal import MultiModalPredictor
+
+# Load the pretrained model
+predictor = MultiModalPredictor.load("bareethul/image-dataset-model")
+
+# Run inference on an image file
+pred = predictor.predict("path/to/your_test_food_image.jpg")
+print("Prediction:", pred)  # 0 = Western cuisine, 1 = Asian cuisine
+