Instructions to use Docty/solacies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docty/solacies with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Docty/solacies") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Docty/solacies") model = AutoModelForImageClassification.from_pretrained("Docty/solacies", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Docty/solacies: direct link, hf CLI and curl.
- Browser
- Download file 940 Bytes
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https://huggingface.co/Docty/solacies/resolve/main/README.md
- Command line
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hf download hf://Docty/solacies/README.md
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curl -L -o README.md https://huggingface.co/Docty/solacies/resolve/main/README.md
940 Bytes
metadata
base_model: google/vit-base-patch16-224-in21k
library_name: transformers
license: creativeml-openrail-m
inference: true
tags:
- image-classification
Image Classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Docty/solaices dataset.
You can find some example images in the following.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]



