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_type": "onnx", | |
| "task": "steganography-detection", | |
| "architecture": "multi-stream-cnn", | |
| "input_names": ["meta", "alpha", "lsb", "palette", "format_features", "content_features", "bit_order"], | |
| "output_names": ["stego_logits", "method_logits", "method_id", "method_probs", "embedding"], | |
| "method_labels": ["alpha", "palette", "lsb.rgb", "exif", "raw"], | |
| "image_size": 256, | |
| "num_classes": 2, | |
| "num_methods": 5 | |
| } |