Instructions to use ViTAMIn-O/PDLO_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViTAMIn-O/PDLO_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ViTAMIn-O/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("ViTAMIn-O/PDLO_classifier") model = AutoModelForImageClassification.from_pretrained("ViTAMIn-O/PDLO_classifier") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - biology | |
| - organoids | |
| - vitamin-o | |
| library_name: transformers | |
| # ViTAMIn-O Custom Organoid Model | |
| This model was trained using the **ColabViTAMIn-O** code-free infrastructure. | |
| ## Model Details | |
| * **Base Architecture:** `ViTAMIn-O/ViTAMIn-O_base_model` | |
| * **Task Type:** `Classification` | |
| * **Repository:** `ViTAMIn-O/PDLO_classifier` | |
| ## Training Hyperparameters | |
| * **Seed:** `46` | |
| * **Epochs:** `20` | |
| * **Batch Size:** `32` | |
| ## Evaluation Metrics (Test Set) | |
| * **Accuracy:** `0.9625` | |
| * **Global AUROC:** `0.9938` | |
| *This model card was auto-generated by the ViTAMIn-O pipeline to ensure reproducibility and open-science transparency.* | |