Instructions to use defefekt/ViTAMIn-O with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use defefekt/ViTAMIn-O with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="defefekt/ViTAMIn-O") 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/ViTAMIn-O") model = AutoModelForImageClassification.from_pretrained("defefekt/ViTAMIn-O") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - biology | |
| - organoids | |
| - vitamin-o | |
| library_name: transformers | |
| license: mit | |
| # ViTAMIn-O Generalist Model | |
| This is the official baseline model, trained and used for inference in the corresponding paper: | |
| `ViTAMIn-O: Democratizing computer vision-based machine learning for stem cell research` | |
| ## Model Details | |
| * **Base Architecture:** `microsoft/swin-large-patch4-window7-224` | |
| * **Task Type:** `Classification` | |
| * **Repository:** `defefekt/ViTAMIn-O` | |
| ## Training Hyperparameters | |
| * **Seed:** `42` | |
| * **Epochs:** `50` | |
| * **Batch Size:** `64` | |