Instructions to use internetoftim/dinov2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internetoftim/dinov2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="internetoftim/dinov2-base")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("internetoftim/dinov2-base") model = AutoModel.from_pretrained("internetoftim/dinov2-base") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
To fine-tuning Details
nielsr/dinov2-base # pre-trained model from which to fine-tune
Graphcore/vit-base-ipu # config specific to the IPU (Used POD4)
How to use in IPU: https://huggingface.co/internetoftim/dinov2-base/blob/main/image_classification-dinov2-base.ipynb
Run the notebooks in this repository:
Poplar SDK: v3.2.1
Dataset:
load a custom dataset from local/remote files or folders using the ImageFolder feature option 1: local/remote files (supporting the following formats: tar, gzip, zip, xz, rar, zstd) url = "https://madm.dfki.de/files/sentinel/EuroSAT.zip" files = list(Path(dataset_dir).rglob("EuroSAT.zip"))
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