# Training your first model

## Downloading data

Download datasets provided by nerfstudio. We support the major datasets and allow users to create their own dataset, described in detail [here](./custom_dataset.md).

```
ns-download-data --dataset=blender
ns-download-data --dataset=nerfstudio --capture=poster
```

:::{admonition} Tip
:class: info

Use `ns-download-data --help` to view all currently available datasets.
:::

The resulting script should download and unpack the dataset as follows:

```
|─ nerfstudio/
   ├─ data/
   |  ├─ blender/
   |     ├─ fern/
   |     ├─ lego/
         ...
      |- nerfstudio/
         |- poster
         ...
```

## Training a model

See which models are available.

```bash
ns-train --help
```

Run a vanilla nerf model.

```bash
ns-train vanilla-nerf
```

Run a nerfacto model.

```bash
ns-train nerfacto
```

Run a nerfacto model with different data and port.

```
ns-train nerfacto --vis viewer --data data/nerfstudio/poster --viewer.websocket-port 7007
```

Run a nerfacto model and load the latest checkpoint to resume training.

```
ns-train nerfacto --vis viewer --data data/nerfstudio/poster --trainer.load_dir outputs/data-nerfstudio-poster/nerfacto/{timestamp}/nerfstudio_models
```

:::{admonition} Warning
:class: warning

- You may have to change the ports, and be sure to forward the "websocket-port".
- All data configurations must go at the end. In this case, `nerfstudio-data` and all of its corresponding configurations come at the end after the model and viewer specification.
  :::

## Intro to nerfstudio CLI and Configs

Nerfstudio allows customization of training and eval configs from the CLI in a powerful way, but there are some things to understand.

The most demonstrative and helpful example of the CLI structure is the difference in output between the following commands:

```bash
ns-train -h
```

```bash
ns-train nerfacto -h nerfstudio-data
```

```bash
ns-train nerfacto nerfstudio-data -h
```

In each of these examples, the -h applies to the previous subcommand (`ns-train`, `nerfacto`, and `nerfstudio-data`).

In the first example, we get the help menu for the `ns-train` script.

In the second example, we get the help menu for the `nerfacto` model.

In the third example, we get the help menu for the `nerfstudio-data` dataparser.

With our scripts, your arguments will apply to the preceding subcommand in your command, and thus where you put your arguments matters! Any optional arguments you discover from running

```bash
ns-train nerfacto -h nerfstudio-data
```

need to come directly after the `nerfacto` subcommand since these optional arguments only belong to the `nerfacto` subcommand:

```bash
ns-train nerfacto <nerfacto optional args> nerfstudio-data
```

Each script will have some other minor quirks (like the training script dataparser subcommand needing to come after the model subcommand), read up on them [here](../reference/cli/index.md).

## Visualizing training runs

If you are using a fast NeRF variant (ie. Nerfacto/Instant-NGP), we recommend using our viewer. See our [viewer docs](viewer_quickstart.md) for a tutorial on using the viewer. The viewer will allow interactive, real-time visualization of training.

For slower methods where the viewer is not recommended, we default to [Wandb](https://wandb.ai/site) to log all training curves, test images, and other stats. We also support logging with [Tensorboard](https://www.tensorflow.org/tensorboard). We pre-specify default `--vis` options depending on the model.

:::{admonition} Attention
:class: attention

- Currently we only support using a single viewer at a time.
- To toggle between Wandb, Tensorboard, or our Web-based Viewer, you can specify `--vis VIS_OPTION`, where `VIS_OPTION` is one of {viewer,wandb,tensorboard}.
  :::

#### Rendering videos

We also provide options to render out the scene of a trained model with a custom trajectory and save the output to a video.

```bash
ns-render --load-config={PATH_TO_CONFIG} --traj=spiral --output-path=renders/output.mp4
```

While we provide pre-specified trajectory options, `--traj={spiral, interp, filename}` we can also specify a custom trajectory if we specify `--traj=filename --camera-path-filename {PATH}`.

:::{admonition} Tip
:class: info
After running the training, the config path is logged to the terminal under "[base_config.py:263] Saving config to:"
:::

:::{admonition} See Also
:class: seealso
This quickstart allows you to preform everything in a headless manner.
We also provide a web-based viewer that allows you to easily monitor training or render out custom trajectories.
See our [viewer docs](viewer_quickstart.md) for more.
:::

## Evaluating Runs

Calculate the psnr of your trained model and save to a json file.

```bash
ns-eval --load-config={PATH_TO_CONFIG} --output-path=output.json
```

We also provide a train and evaluation script that allows you to do benchmarking on the classical Blender dataset (see our [benchmarking workflow](../developer_guides/debugging_tools/benchmarking.md)).