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
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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`
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