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