Update README.md

README.md

@@ -3,8 +3,21 @@ license: cc-by-4.0
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 # TACDEC-model
-This is a simple model with weights to reproduce the model used in TACDEC-paper.
+This is a simple model with weights and reproducible code for the results in TACDEC-paper.
+
+What you can find in this repo is:
+- The simple [model](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/model.py?download=true) used in the TACDEC-paper
+- The [weights](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/simple_model_weights.pt?download=true) used in the proof-of-concept section in the TACDEC-paper
+- 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.
+- 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].
+
+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.
+
+
+If you hold more interest in DINOv2, the **feature_extraction.ipynb** could hold good value.
+
+In both notebooks, there should be good enough documentation, but should you have any questions [TACDEC](https://huggingface.co/datasets/SimulaMet-HOST/TACDEC).
 
 ## More information
-For more information about the dataset: 
+For any other information or information about the dataset: 
 [TACDEC](https://huggingface.co/datasets/SimulaMet-HOST/TACDEC)