v0.1 to v0.1.0

---

name: tao-train-depth-anything-v2

description: Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts

per-pixel depth from single RGB images. Use when training, evaluating, exporting, or running inference for a TAO

monocular depth model. Trigger phrases include "train monocular depth", "DepthAnything v2", "metric depth from single

image", "monocular depth estimation".

license: Apache-2.0

compatibility: Requires docker + nvidia-container-toolkit.

metadata:

author: NVIDIA Corporation

allowed-tools: Read Bash

tags:

- monocular

- depth

- estimation

---

# Depth Net Mono

Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images.

## Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references/skill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as `automl_policy: off` for this run only; otherwise default to `auto`. When `automl_policy: auto`, `automl_enabled: true`, and both `schemas/train.schema.json` and `references/spec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.

Non-train actions such as `evaluate`, `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.

## Workflow

### Prerequisites — data accessibility

Your dataset (RGB images + GT depth files) must be reachable from inside the container:

- **Direct `docker run`** (e.g. local testing): mount the host dataset root read-only at the same in-container path:

```

docker run ... -v <host_data_root>:<host_data_root>:ro <container> ...

```

The same accessibility requirement applies to the `<output_dir>` written by all actions.

### Step 1 — Annotation file

Per-line annotation file referenced by `data_sources[*].data_file`:

| Columns | Format | Use |

|---|---|---|

| 1 | `<image>` | Mono inference (no GT) |

| 2 | `<image> <gt_depth>` | Mono with GT |

If you already have one, point to it. Otherwise generate via `depth_net convert`:

```

depth_net convert -e <convert_spec.yaml>

```

`convert_spec.yaml` template:

```yaml

data_root: <directory whose immediate children are scene/sample folders that contain your image+depth files; convert walks data_root recursively but expects per-scene subdirectories at one level below>

image_dir_pattern: [<substring matching left/RGB image paths>]

depth_dir_pattern: [<substring matching GT depth paths>]

image_extension: '' # optional .endswith filter, e.g. '.jpg'

depth_extension: '' # optional, swapped during depth derivation, e.g. '.png'

split_ratio: 0.0 # 0.0/1.0 = test-only; 0.8 = 80/20 train+val

```

`convert` walks `data_root` recursively, selects paths whose path-string contains *all* substrings in `image_dir_pattern` (AND-filter), then derives the depth path by replacing `image_dir_pattern[0]` with `depth_dir_pattern[0]` and `image_extension` with `depth_extension`. Inspect your dataset's directory layout and identify the substring distinguishing RGB images from depth files (e.g. `rgb_` vs `sync_depth_`).

`data_root` must point at the parent that contains the per-scene subdirectories (e.g. for NYU eval, use `/data/nyu_v2/eval/test`, not `/data/nyu_v2/eval/test/bathroom` — the latter limits the walk to a single scene). Always include the leading dot in `image_extension` / `depth_extension` (e.g. `'.jpg'` not `'jpg'`); the substring swap is form-sensitive and a mismatch silently corrupts derived paths.

### Step 2 — Pair `model_type` and `dataset_name` based on your data

Default — generic class for each task:

| Data category | `model_type` | `dataset_name` |

|---|---|---|

| Disparity-encoded data (pixels) | `RelativeDepthAnything` | `RelativeMonoDataset` |

| Metric depth (meters) | `MetricDepthAnything` | `MetricMonoDataset` |

| Mono inference (no GT, any image) | matches train choice | `RelativeMonoDataset` or `MetricMonoDataset` |

Dataset-specific class — switch when the data needs preprocessing the generic class does not perform:

| Special case | `model_type` | `dataset_name` | What the class adds |

|---|---|---|---|

| NYU `sync_depth_*.png` (raw uint16 millimetres) — relative | `RelativeDepthAnything` | `NYUDV2Relative` | mm→m unit conversion + Eigen evaluation crop |

| NYU `sync_depth_*.png` (raw uint16 millimetres) — metric | `MetricDepthAnything` | `NYUDV2` | same |

Using a generic class on data that requires unit conversion (e.g. raw NYU uint16 PNGs) results in an empty valid mask and silent `train_loss = NaN`. Match the class to your data's encoding.

- `model.model_type` from Step 2

- `dataset.<...>.data_sources[*].dataset_name` from Step 2

- `data_sources[*].data_file` with the path from Step 1 (S3 path under SDK runner, host path for direct docker)

For mono training set `train.precision: fp32` (recommended) or `bf16` (Ampere SM80+, alternative).

### Step 4 — Run

```

docker run --gpus 'device=0' --shm-size 16G --ipc=host \

--user $(id -u):$(id -g) \

-v <data_root>:<data_root>:ro \

-v <output_dir>:<output_dir> \

<container> \

depth_net <action> -e <spec.yaml>

```

Without `--user $(id -u):$(id -g)` the container writes outputs as `nobody:nogroup`, blocking host-side cleanup and retry.

### Step 5 — Verify

- Container exit code 0

- `status.json` `kpi` block populated

- For `evaluate` / `inference`: artifacts under `results_dir`

## Training Requirements

- **Valid `dataset_name` values for mono `data_sources`** (case-insensitive): `ThreeDVLM`, `FSD`, `NvCLIP`, `IssacStereo`, `Crestereo`, `Middlebury`, `NYUDV2`, `NYUDV2Relative`, `RelativeMonoDataset`, `MetricMonoDataset`. `NYUDV2` carries metric depth GT (meters) — pair with `MetricDepthAnything`; `NYUDV2Relative` is the same data with relative-depth conventions — pair with `RelativeDepthAnything`.

- **Monitoring metric:** val/loss

### Per-Action Dataset Requirements

| Action | Spec Key | Source | Files | List? |

|---|---|---|---|---|

| evaluate | dataset.test_dataset.data_sources | eval_dataset | data_file: annotations.txt + dataset_name | Yes |

| inference | dataset.infer_dataset.data_sources | inference_dataset | data_file: annotations.txt + dataset_name | Yes |

| quantize | dataset.train_dataset.data_sources | train_datasets | data_file: annotations.txt + dataset_name | Yes |

| quantize | dataset.val_dataset.data_sources | eval_dataset | data_file: annotations.txt + dataset_name | Yes |

| quantize | dataset.quant_calibration_dataset.images_dir | train_datasets | images.tar.gz | No |

| train | dataset.train_dataset.data_sources | train_datasets | data_file: annotations.txt + dataset_name | Yes |

| train | dataset.val_dataset.data_sources | eval_dataset | data_file: annotations.txt + dataset_name | Yes |

## Eval Dataset

Optional. Val dataset configured via `dataset.val_dataset.data_sources` (each entry needs `data_file` and `dataset_name`).

## Important Parameters

## Multi-GPU / Multi-Node

**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).

| Spec Key | Description | Default |

|----------|-------------|---------|

| `train.num_gpus` | Number of GPUs | 1 |

| `train.gpu_ids` | GPU device indices | [0] |

| `train.num_nodes` | Number of nodes | 1 |

| `train.distributed_strategy` | `ddp` or `fsdp` | `ddp` |

- `ddp` with activation checkpointing: `find_unused_parameters=False`

- `ddp` without: `find_unused_parameters=True`

- `fsdp` forces precision to FP16

**Multi-node env vars** (set by orchestrator): `WORLD_SIZE`, `NODE_RANK`, `MASTER_ADDR`, `MASTER_PORT`, `NUM_GPU_PER_NODE`.

## Export / TRT Defaults

- TRT data types: FP32, BF16 (Ampere SM80+). FP16 is not supported for the ViT-L mono backbone.

- Recommended TRT precision: `bf16`. Use `fp32` if BF16 hardware is unavailable.

## Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 24GB+ VRAM per GPU. ViT-Large encoder is memory intensive. Use `fp32` (recommended) or `bf16` (Ampere SM80+, alternative) for training. Activation checkpointing is available for larger inputs.

## Error Patterns

## Spec Param / Parent Model Inference