Image Classification
Transformers
Safetensors
English
siglip
Corals
Bleach
Healthy
Classification
Siglip2
ViT
Instructions to use prithivMLmods/Coral-Health with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Coral-Health with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Coral-Health") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Coral-Health") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Coral-Health") - Notebooks
- Google Colab
- Kaggle
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# **Coral-Health**
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> **Coral-Health** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify coral reef images into two health conditions using the **SiglipForImageClassification** architecture.
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- ViT
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# **Coral-Health**
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> **Coral-Health** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify coral reef images into two health conditions using the **SiglipForImageClassification** architecture.
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