t-SNE example plot

README.md

@@ -77,15 +77,15 @@ This script evaluates your trained model by providing mAP and MR1 metrics and an
 2. Run your query data through extract_csi_features. In the hparams.yaml file for the feature extraction, turn off all augmentation. See data/covers80_testset/hparams.yaml for an example configuration to treat covers80 as the query data:<br> `python3 -m tools.extract_csi_features data/covers80_testset`<br>
 The important output from that is full.txt and the cqt_feat subfolder's contents.
 3. Run the evaluation script:<br>
-`python3 -m tools.eval_testset egs/covers80 data/covers80_testset/full.txt data/covers80_testset/full.txt -plot_name="tSNE.png" -dist_name='distmatrix' -test_only_labels='data/covers80/dev-only-song-ids.txt'`
+`python3 -m tools.eval_testset egs/covers80 data/covers80_testset/full.txt data/covers80_testset/full.txt -plot_name="egs/covers80/tSNE.png" -dist_name='distmatrix' -test_only_labels='data/covers80/dev-only-song-ids.txt'`
 
 CoverHunter only shared an evaluation example for the case when query and reference data are identical, presumably to do a self-similarity evaluation of the model. But there is an optional 4th parameter for `query_in_ref_path` that would be relevant if query and reference are not identical. See the "query_in_ref" heading below under "Input and Output Files."
 
 The optional `plot_name` argument is a path where you want to save the t-SNE plot output. Note that your query and reference files must be identical in this case (to do a self-similarity evaluation).
 
 The optional `test_only_labels` argument is a path to the text file generated by `extract_csi_features.py` if its hyperparamaters asked for some song_ids to be reserved exclusively for the test aka "dev" dataset. The t-SNE plot will then mark those for you to see how well your model can cluster classes (song_ids) it has never seen before.
 
-![t-SNE plot for Covers80 training with 3 song_ids in test held out from training data](https://drive.google.com/file/d/1cBewle3Wj58OtImNuBLwyVmMiVTzXuWg/view?usp=sharing)
+![t-SNE plot for Covers80 training with 3 song_ids in test held out from training data](tSNE-example.png)
 
 The optional `dist_name` argument is a path where you want to save the distance matrix and ref labels so that you can study the results separately, such as perhaps doing custom t-SNE plots, etc.