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Running
on
Zero
# Single-modality | |
## Installation | |
Please follow the installation instructions in [INSTALL](./INSTALL.md). | |
## Datasets | |
You can find the dataset instructions in [DATASET](./DATASET.md). | |
## Model ZOO | |
You can find all the models and the scripts in [MODEL_ZOO](./MODEL_ZOO.md). | |
## Pre-Training | |
We use [InternVL](https://github.com/OpenGVLab/InternVL/) and [VideoMAEv2](https://github.com/OpenGVLab/VideoMAEv2) pretrained models as teachers by default | |
For training, you can simply run the pretraining scripts in `scripts/pretraining` as follows: | |
```shell | |
bash ./scripts/pretraining/1B_pt.sh | |
``` | |
:warning: **Notes:** | |
1. Chage `DATA_PATH` to your data path before running the scripts. | |
2. `--sampling_rate` is set to 1 for **sprase sampling**. | |
3. The latest checkpoint will be automatically saved while training, thus we use a large `--save_ckpt_freq`. | |
4. For InternVideo2-1B and 6B, we use InternVL-C-13B and VideoMAEv2-g. | |
## Finetuning | |
For finetuning, you can simply run the pretraining scripts in `scripts/finetuning` as follows: | |
```shell | |
bash ./scripts/finetuning/full_tuning/k400/1B_ft_k710_ft_k400_f8.sh | |
``` | |
:warning: **Notes:** | |
1. Chage `DATA_PATH` And `PREFIX` to your data path before running the scripts. | |
2. Chage `MODEL_PATH` to your model path. | |
3. Set `--use_checkpoint` and `--checkpoint_num` to save GPU memory. | |
4. The best checkpoint will be automatically evaluated with `--test_best`. | |
5. Set `--test_num_segment` and `--test_num_crop` for different evaluation strategies. | |
6. To only run evaluation, just set `--eval`. | |