| # CommentarySet | |
| We have provided the code `Code`, and the complete CommentarySet `data`. | |
| ## Data Structure: | |
| For training and validating, you should arrange the dataset and code in the following structure: | |
| - YOUR_MODEL_ROOT_DIRECTORY | |
| - data | |
| - commentary | |
| - atheletics_final.json | |
| - basketball_final.json | |
| - ... | |
| - video | |
| - athletics | |
| - 001 | |
| - 5.mp4 | |
| - 7.mp4 | |
| - ... | |
| - ... | |
| - basketball | |
| - ... | |
| - train.json | |
| - test.json | |
| - Video LLMs Official Code(e.g VILA) | |
| - metric_six_dimensional.py | |
| - metric_traditional_gpt.py | |
| - metric_traditional.py | |
| - eval.sh | |
| - run.sh | |
| ## Code | |
| The code includes: | |
| 1. `run_exp.py` files for 8 baseline models, enabling the use of VideoLLM to generate commentary. | |
| 2. Three metric scripts `metric_six_dimensional.py`,`metric_traditional_gpt.py`,`metric_traditional.py` that implement the metrics mentioned in our paper. | |
| 3. Shell scripts: `run.sh` and `eval.sh`, which can be used directly for model inference and result evaluation. | |
| Here are some suggestions for you before running the code. | |
| ### Inference Process | |
| 1. **Download the Official VideoLLM Code:** Download the official code for the corresponding VideoLLM models, and place the provided `run_exp.py` file in the appropriate path as specified below:(You can also use other models, but you need do create their `run_exp.py`.) | |
| - **Chat-UniVi**: `./Chat-UniVi/` | |
| - **InternVL 1.5**: `./InternVL/VL1_5/` | |
| - **InternVL 2.0**: `./InternVL/VL2/` | |
| - **Kangaroo**: `./Kangaroo-main/` | |
| - **LLaVA-NeXT**: `./LLaVA-NeXT/playground/demo/` | |
| - **LongVA**: `./LongVA/` | |
| - **Video-LLaVA**: `./Video-LLaVA/videollava/serve/` | |
| - **VILA**: `./VILA/` | |
| 2. **Download the Checkpoints:** Download the checkpoints to the given path in every `run_exp.py` file. | |
| 3. **Set Up the Environment:** Configure the environment based on the information provided by the official model documentation. | |
| 4. **Set Up `run.sh`:** Configure the environment variables, `gpu_id`, and `Path_To_Your_Models_run_exp` in `run.sh` accordingly. | |
| 5. After that, use **run.sh** for commentary generation by: | |
| ```bash | |
| ./run.sh | |
| ``` | |
| 6. Finally, the experiment is completed, the commentary generated by the model will be saved in | |
| ``` | |
| ./data/res/model_name/sports_name.json. | |
| ``` | |
| ### Evaluation Process | |
| 1. **Configure Metrics:** In the three metric scripts, set the `base_url` and `api_key` in the client at the beginning of `metric_six_dimensional.py`, `metric_traditional_gpt.py`, or `metric_traditional.py`. | |
| 2. In the `eval.sh` file, lines `6-11` allow you to choose the metric you want to use from a total of 3 metrics by uncommenting the corresponding line. | |
| 3. Set the config `model_name` in `eval.sh`, to the name of the model you want to test in the corresponding line (it should be the same as the model name in the folder where the inference results are stored). | |
| 4. Run the following command to start the evaluation process. The eval.sh script will automatically evaluate the model commentary based on the list in test.json. | |
| ```bash | |
| ./eval.sh | |
| ``` | |
| ### Fine-tuning On CommentarySet: | |
| After preparing the dataset following the structure mentioned above, you can finetune on our **train.json**. | |
| ## data | |
| We have provided all the data in CommentarySet. Folder `commentary` contains information about each clip, folder `video` includes all clips in `.mp4` format, and `test.json` and `train.json` record the complete information of the two subsets. | |
| When running the code, place the `data` in the root directory (`./data`). | |