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@@ -11,7 +11,7 @@ What you can find in this repo is:
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  - A first notebook, [feature_extraction.ipynb](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/feature_extraction.ipynb?download=true), that contains a feature extraction process using DINOv2.
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  - A second notebook, [train_classifier.ipynb](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/train_classifier.ipynb?download=true), that uses the features that were either extracted using the first notebook, or downloaded directly from (TACDEC repo)[https://huggingface.co/datasets/SimulaMet-HOST/TACDEC].
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- We highly recommend downloading the already extracted and concatenated features, together with the concatenated labels. If you just wish to try the dataset/model, you would then just have to run the second notebook.
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  If you hold more interest in DINOv2, the **feature_extraction.ipynb** could hold good value.
 
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  - A first notebook, [feature_extraction.ipynb](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/feature_extraction.ipynb?download=true), that contains a feature extraction process using DINOv2.
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  - A second notebook, [train_classifier.ipynb](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/train_classifier.ipynb?download=true), that uses the features that were either extracted using the first notebook, or downloaded directly from (TACDEC repo)[https://huggingface.co/datasets/SimulaMet-HOST/TACDEC].
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+ We highly recommend downloading the already extracted and concatenated features, together with the concatenated labels if just wish to try the dataset/model. You would then just have to run the second notebook.
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  If you hold more interest in DINOv2, the **feature_extraction.ipynb** could hold good value.