Instructions to use defefekt/PDLO_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use defefekt/PDLO_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="defefekt/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("defefekt/PDLO_Classifier") model = AutoModelForImageClassification.from_pretrained("defefekt/PDLO_Classifier") - Notebooks
- Google Colab
- Kaggle
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tags:
- image-classification
- biology
- organoids
- vitamin-o
library_name: transformers
license: mit
---
# ViTAMIn-O Custom Organoid Model
This model was trained using the **ColabViTAMIn-O** code-free infrastructure.
## Model Details
* **Base Architecture:** `defefekt/ViTAMIn-O`
* **Task Type:** `Classification`
* **Repository:** `defefekt/PDLO_Classifier`
## Training Hyperparameters
* **Seed:** `42`
* **Epochs:** `20`
* **Batch Size:** `32`
## Evaluation Metrics (Test Set)
* **Accuracy:** `0.9250`
* **Global AUROC:** `0.9881`
*This model card was auto-generated by the ViTAMIn-O pipeline to ensure reproducibility and open-science transparency.* |