Update README.md

README.md

@@ -1,36 +1,104 @@
 ---
+language: 
+- en
 tags:
 - automl
 - image-classification
 - autogluon
-- cnn
 - cmu-course
-language: 
-- en
-license: mit
 datasets:
 - keerthikoganti/lipstick-image-dataset
 metrics:
-- accuracy
-- f1
+- type: accuracy
+- type: f1
 model-index:
-- name: nn_automl_model
+- name: Lipstick Detection (Neural Network AutoML)
   results:
   - task:
       type: image-classification
-      name: Image Classification
+      name: Binary Image Classification
     dataset:
       name: keerthikoganti/lipstick-image-dataset
-      type: image
+      type: classification
+      split: augmented
     metrics:
     - type: accuracy
-      value: 0.97
+      value: 1.00
     - type: f1
-      value: 0.96
+      value: 1.00
+  - task:
+      type: image-classification
+      name: Binary Image Classification
+    dataset:
+      name: keerthikoganti/lipstick-image-dataset
+      type: classification
+      split: original
+    metrics:
+    - type: accuracy
+      value: 0.93
+    - type: f1
+      value: 0.93
+---
+
+# Model Card for Lipstick Detection (Neural Network AutoML)
+
+This model performs **binary classification** of images into **lipstick** (1) vs. **no lipstick** (0).  
+It was trained with **AutoGluon Multimodal AutoML**, which automatically explored different **neural network backbones** (ResNet18, ResNet34, EfficientNet-B0) under a fixed budget with early stopping.  
+The best-performing backbone selected was **EfficientNet-B0**.
+
 ---
 
-# Model Card for nn_automl_model (Lipstick Image Classifier)
+## Model Details
+
+### Model Description
+- **Developed by:** Xinxuan Tang (CMU)  
+- **Dataset curated by:** Keerthi Koganti (CMU)  
+- **Model type:** AutoML neural network (best = EfficientNet-B0)  
+- **Language(s):** N/A (image dataset)  
+- **Finetuned from:** `timm/efficientnet_b0` pretrained weights  
+
+### Model Sources
+- **Repository:** [Hugging Face Model Repo](https://huggingface.co/)  
+- **Dataset:** [keerthikoganti/lipstick-image-dataset](https://huggingface.co/datasets/keerthikoganti/lipstick-image-dataset)
+
+---
+
+## Uses
+
+### Direct Use
+- Educational practice in **binary image classification**.  
+- Experimenting with AutoML search over neural architectures.  
+
+### Downstream Use
+- Could be adapted for **teaching transfer learning** workflows.  
+
+### Out-of-Scope Use
+- **Not suitable for real-world cosmetics applications**.  
+- Not for deployment in automated decision-making or safety-critical contexts.  
+
+---
+
+## Bias, Risks, and Limitations
+
+- **Small dataset**: limited original images, heavy reliance on synthetic augmentation.  
+- **Domain bias**: images are from a single source/product and background setup.  
+- **Synthetic augmentation**: does not capture real-world variation in lighting, product types, or diversity of appearances.  
+
+### Recommendations
+Use primarily for **teaching and demonstration** purposes.  
+Do not generalize conclusions beyond this dataset.  
+
+---
+
+## How to Get Started with the Model
+
+```python
+from autogluon.multimodal import MultiModalPredictor
+import pandas as pd
+
+# Load trained predictor
+predictor = MultiModalPredictor.load("autogluon_efficientnet_b0/")
 
-## Model Summary
-This model performs **binary classification** (Lipstick vs. No Lipstick) using CNN backbones (ResNet, EfficientNet) optimized with **AutoGluon Multimodal**.  
-Trained on the `keerthikoganti/lipstick-image-dataset`.
+# Run inference on a new image
+test_data = pd.DataFrame([{"image": "example.jpg"}])
+print(predictor.predict(test_data))