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
metadata
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.