Instructions to use Sean-xyt/LEGO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sean-xyt/LEGO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sean-xyt/LEGO") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sean-xyt/LEGO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Evaluation results | |
| Checkpoint SHA-256: `7602167b44fadd480b97b034001f6926143946945eb08e066866aecb84a50d07` | |
| Reverified on 2026-07-13 with the current `run.test` release entry point and | |
| the complete local benchmark data. | |
| - AIGIBench (24-subset macro ACC, ProGAN excluded): **0.8021** | |
| - Chameleon ACC: **0.8358** | |
| - Chameleon AUC: **0.9241** | |
| - Chameleon AP: **0.8989** | |
| - Chameleon EER: **0.1497** | |
| The AIGIBench aggregate follows the paper protocol and excludes ProGAN because | |
| that generator family is represented in training. Chameleon is evaluated only | |
| after training and is never used for checkpoint selection. | |