Instructions to use defefekt/PDLO_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use defefekt/PDLO_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="defefekt/PDLO_Classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("defefekt/PDLO_Classifier") model = AutoModelForImageClassification.from_pretrained("defefekt/PDLO_Classifier") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - biology | |
| - organoids | |
| - vitamin-o | |
| library_name: transformers | |
| license: mit | |
| # ViTAMIn-O Custom Organoid Model | |
| This model was trained using the **ColabViTAMIn-O** code-free infrastructure. | |
| ## Model Details | |
| * **Base Architecture:** `defefekt/ViTAMIn-O` | |
| * **Task Type:** `Classification` | |
| * **Repository:** `defefekt/PDLO_Classifier` | |
| ## Training Hyperparameters | |
| * **Seed:** `42` | |
| * **Epochs:** `20` | |
| * **Batch Size:** `32` | |
| ## Evaluation Metrics (Test Set) | |
| * **Accuracy:** `0.9250` | |
| * **Global AUROC:** `0.9881` | |
| *This model card was auto-generated by the ViTAMIn-O pipeline to ensure reproducibility and open-science transparency.* |