### Run Initial Training Setup for T-DEED

Source: https://github.com/soccernet/sn-teamspotting/blob/main/README.md

Execute this script for the first run to partition videos into clips and store necessary information for faster data loading. Set 'store_mode' to 'store'.

```python
python /train_tdeed_bas.py --model SoccerNetBall_baseline --store_mode store
```

--------------------------------

### Train T-DEED Model

Source: https://github.com/soccernet/sn-teamspotting/blob/main/README.md

Run this script after the initial setup to train the T-DEED model. Set 'store_mode' to 'load' for training on prepared data splits. The script handles training, validation, testing, and prediction generation.

```python
python /train_tdeed_bas.py --model SoccerNetBall_baseline --store_mode load
```

--------------------------------

### SoccerNet Ball Action Spotting Frame Structure

Source: https://github.com/soccernet/sn-teamspotting/blob/main/data/soccernetball/README.md

Illustrates the expected directory and file naming convention for extracted frames of the SoccerNet Ball Action Spotting dataset.

```text
data-folder
└───england_efl
    └───2019-2020
        └───2019-10-01 - Blackburn Rovers - Nottingham Forest
        |frame0.jpg
        |frame1.jpg
        |...
        └───2019-10-01 - Brentford - Bristol City
        |frame0.jpg
        |frame1.jpg
        |...
```

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