Instructions to use macroadster/starlight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macroadster/starlight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="macroadster/starlight") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("macroadster/starlight", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model Card: Starlight Unified Model 2025
Model Overview
- Task: Detection / Extraction
- Architecture: Unified CNN-based Encoder-Decoder with Residual Blocks
- Input: 256x256 RGB/RGBA or metadata
- Output:
- Detector: sigmoid probability
- Extractor: variable-length byte sequence
Training
- Dataset: Combined submissions (grok, gemini, claude, chatgpt, sample)
- Epochs: 50
- Batch Size: 16
- Optimizer: Adam
- Loss: BCE + MSE (detector), CrossEntropy (extractor)
Performance
| Metric | Value |
|---|---|
| Accuracy | 96.3% |
| AUC-ROC | 0.996 |
| F1 Score | 0.982 |
| Extraction BER | 0.003 |
Steganography Coverage
lsb,alpha,dct,exif,eoi,palette
Inference Speed
- CPU: 12 ms/image
- GPU: 2.1 ms/image
License
- Model: Apache 2.0
- Code: MIT
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