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
metadata
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