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Update tutorial file names
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Nima committed Oct 21, 2021
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8 changes: 4 additions & 4 deletions src/tutorials/README.md
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Expand Up @@ -111,8 +111,8 @@ SOMA is implemented in PyTorch and the training code benefits from the
PTL standardizes the training procedure and enables easy multi GPU training.

We provide Jupyter notebooks to demonstrate use cases of SOMA with hands-on examples:
- [Running SOMA on SOMA dataset](src/tutorials/runing_soma_on_soma_dataset.ipynb).
- [Solving an already labeled dataset](src/tutorials/solving_an_already_labeled_mocap.ipynb).
- [Running SOMA on SOMA dataset](src/tutorials/run_soma_on_soma_dataset.ipynb).
- [Solving an already labeled dataset](src/tutorials/solve_labeled_mocap.ipynb).
- [Label Priming](src/tutorials/label_priming.ipynb).
- [Running SOMA on SOMA dataset](src/tutorials/runing_soma_on_soma_dataset.ipynb).
- [Producing Synthetic MoCap for Evaluation](src/tutorials/produce_synthetic_mocap.ipynb)
- [Run SOMA on the SOMA dataset](src/tutorials/run_soma_on_soma_dataset.ipynb).
- [Run SOMA Ablation Study](src/tutorials/ablation_study.ipynb)
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Expand Up @@ -19,7 +19,7 @@
"\n",
"Furthermore, note that our goal is to simply mosh a dataset without considering comparability with other mosh runs;\n",
"i.e. you cannot compare these results with SOMA experiments. For that refer to special mosh run presented in\n",
"[here](tutorial_run_soma_on_soma_dataset.ipynb).\n",
"[here](run_soma_on_soma_dataset.ipynb).\n",
"\n",
"Finally, the same code can be used for rendering the MoSh results as well. Enjoy!"
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