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language:
- en
tags:
- automl
- image-classification
- autogluon
- cmu-course
datasets:
- keerthikoganti/lipstick-image-dataset
metrics:
- type: accuracy
- type: f1
model-index:
- name: Lipstick Detection (Neural Network AutoML)
results:
- task:
type: image-classification
name: Binary Image Classification
dataset:
name: keerthikoganti/lipstick-image-dataset
type: classification
split: augmented
metrics:
- type: accuracy
value: 1.00
- type: f1
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 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/")
# Run inference on a new image
test_data = pd.DataFrame([{"image": "example.jpg"}])
print(predictor.predict(test_data))
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