Instructions to use prithivMLmods/Multisource-121-DomainNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Multisource-121-DomainNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Multisource-121-DomainNet") 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/Multisource-121-DomainNet") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Multisource-121-DomainNet") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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datasets:
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- TNILab/DomainNet_FL
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---
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# **Multisource-121-DomainNet**
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> **Multisource-121-DomainNet** 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 images into 121 domain categories using the **SiglipForImageClassification** architecture.
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license: apache-2.0
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datasets:
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- TNILab/DomainNet_FL
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language:
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- en
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base_model:
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- google/siglip2-base-patch16-224
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pipeline_tag: image-classification
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library_name: transformers
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tags:
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- Multisource-121-DomainNet
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---
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# **Multisource-121-DomainNet**
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> **Multisource-121-DomainNet** 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 images into 121 domain categories using the **SiglipForImageClassification** architecture.
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