---
name: tao-train-visual-changenet
description: Trains, evaluates, exports, and runs inference for Visual ChangeNet models used in AOI defect detection, comparing image pairs for PASS/NO_PASS classification or change-segmentation masks.
license: Apache-2.0
---

# Visual ChangeNet

Visual ChangeNet is a TAO Toolkit model for visual inspection and defect detection. It supports two tasks:

- **Classify** — Binary image classification using a siamese-style architecture with a shared backbone (C-RADIO ViT) and a learnable difference module. Compares image pairs to classify defects as PASS/NO_PASS.
- **Segment** — Pixel-level change segmentation using a ViT-Large NVDINOv2 backbone. Compares before/after image pairs to produce a binary change mask.

The backbone weight (`c_radio_v2_vit_base_patch16_224`) is the `nvidia/C-RADIOv2-B` model from HuggingFace, distributed as `model.safetensors` (~393 MB). **The TAO 7.0.0-rc container does not auto-fetch from HF URLs** — `ptm_utils.load_pretrained_weights()` hands the `pretrained_backbone_path` value to `torch.load(path)` / `safetensors.torch.load_file(path)` directly. Passing an `https://huggingface.co/...` URL or a repo id produces `FileNotFoundError` and the run fails with `Execution status: FAIL` within a few seconds. Stage the file locally before launch:

```bash
python3 -c "from huggingface_hub import hf_hub_download; import shutil; \
shutil.copy(hf_hub_download('nvidia/C-RADIOv2-B', 'model.safetensors'), '<workspace>/backbone/c_radio_v2_b.safetensors')"
```

Mount it into the container (`-v <workspace>/backbone/c_radio_v2_b.safetensors:/data/pretrained_models/C-RADIOv2_B.safetensors`) and set the spec `model.backbone.pretrained_backbone_path` to the container path. `HF_TOKEN` is only needed at staging time, not at training time.

Segment specs use `model.backbone.type: vit_large_nvdinov2` and the NVDINOv2
checkpoint family. Keep the checkpoint architecture aligned with the backbone
type: `NV_DINOV2_518_16_256.ckpt` is compatible with the packaged segment
templates, but it must not be used with `fan_small_12_p4_hybrid`. If you switch
to a different segment backbone, use a matching checkpoint or leave
`model.backbone.pretrained_backbone_path` empty for default initialization.

## Dataclass Schemas

Generated TAO Core schemas are packaged in `schemas/<action>.schema.json`, with `schemas/manifest.json` listing available actions. Each generated schema also emits `references/spec_template_<action>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references/skill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas/train.schema.json` and `references/spec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~/tao-core` at runtime; maintainers regenerate schemas/templates before packaging the skill bank.

## 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. Use `automl_policy: on` by default and only expose `on` / `off` in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as `automl_policy: off` for this run only. When `automl_policy: on`, `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 declared by this model skill (`evaluate`, `inference`,
`export`, `quantize`, `segment_evaluate`, and `segment_inference`) stay in this
model skill. Do not present `segment_export` or `segment_quantize` as runnable
parent-skill actions until matching entries are packaged in
`schemas/manifest.json`. Prune and retrain are not declared in the current
parent `references/skill_info.yaml`; do not present them as runnable parent-skill
actions unless the metadata is extended with matching action wiring and schemas.
The per-run `automl_policy` override does not change model metadata.

For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference` for classify and segment variants), read `references/tao-deploy-visual-changenet.md` first. Deploy spec templates live in this skill's `references/` folder with the `spec_template_deploy_*.yaml` prefix.
Deploy requires an exported ONNX artifact as `parent_model`. If no ONNX artifact exists and the main skill does not expose an export action, report deploy as blocked instead of inventing an artifact.

## Training Requirements

Visual ChangeNet has two separate task modes with different dataset types and data source structures.

### Classify

- **Dataset type:** visual_changenet_classify
- **Formats:** default
- **Accepted dataset intents:** training, evaluation, testing, calibration
- **Monitoring metric:** val_loss

#### Per-Action Dataset Requirements (Classify)

The `quantize` and `gen_trt_engine` rows below describe TAO spec data requirements only. They are not parent-skill actions unless the corresponding action is declared in `references/skill_info.yaml` or `deploy/skill_info.yaml`.

| Action | Spec Key | Source | Files | List? |
|---|---|---|---|---|
| train | dataset.classify.train_dataset.images_dir | train_datasets | images.tar.gz | No |
| train | dataset.classify.train_dataset.csv_path | train_datasets | dataset.csv | No |
| train | dataset.classify.validation_dataset.images_dir | eval_dataset | images.tar.gz | No |
| train | dataset.classify.validation_dataset.csv_path | eval_dataset | dataset.csv | No |
| quantize | dataset.classify.train_dataset.images_dir | train_datasets | images.tar.gz | No |
| quantize | dataset.classify.train_dataset.csv_path | train_datasets | dataset.csv | No |
| quantize | dataset.classify.validation_dataset.images_dir | eval_dataset | images.tar.gz | No |
| quantize | dataset.classify.validation_dataset.csv_path | eval_dataset | dataset.csv | No |
| quantize | dataset.classify.quant_calibration_dataset.images_dir | train_datasets | images.tar.gz | No |
| evaluate | dataset.classify.validation_dataset.images_dir | eval_dataset | images.tar.gz | No |
| evaluate | dataset.classify.validation_dataset.csv_path | eval_dataset | dataset.csv | No |
| evaluate | dataset.classify.test_dataset.images_dir | eval_dataset | images.tar.gz | No |
| evaluate | dataset.classify.test_dataset.csv_path | eval_dataset | dataset.csv | No |
| inference | dataset.classify.infer_dataset.images_dir | inference_dataset | images.tar.gz | No |
| inference | dataset.classify.infer_dataset.csv_path | inference_dataset | dataset.csv | No |
| gen_trt_engine | gen_trt_engine.tensorrt.calibration.cal_image_dir | calibration_dataset | images.tar.gz | Yes |

### Segment

- **Dataset type:** visual_changenet_segment
- **Formats:** default
- **Accepted dataset intents:** training, calibration
- **Monitoring metric:** val_loss

Segment uses a paired directory structure (`A/`, `B/`, `list/`, `label/`) instead of CSV + images. The `root_dir` spec key points to the top-level directory containing all four subdirectories.

**Required files per dataset:** `A.tar.gz`, `B.tar.gz`, `list.tar.gz`, `label.tar.gz`

#### Per-Action Dataset Requirements (Segment)

The `quantize` and `gen_trt_engine` rows below describe TAO spec data requirements only. They are not parent-skill actions unless the corresponding action is declared in `references/skill_info.yaml` or `deploy/skill_info.yaml`.

| Action | Spec Key | Source | Files | List? |
|---|---|---|---|---|
| train | dataset.segment.root_dir | train_datasets | (root directory) | No |
| quantize | dataset.segment.root_dir | train_datasets | (root directory) | No |
| quantize | dataset.segment.quant_calibration_dataset.images_dir | train_datasets | (root directory) | No |
| evaluate | dataset.segment.root_dir | train_datasets | (root directory) | No |
| inference | dataset.segment.root_dir | train_datasets | (root directory) | No |
| gen_trt_engine | dataset.segment.root_dir | train_datasets | (root directory) | No |
| gen_trt_engine | gen_trt_engine.tensorrt.calibration.cal_image_dir | calibration_dataset | images.tar.gz | Yes |

### Typical Spec Overrides

Data source overrides are **mandatory for every action** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.

```python
S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"
```

**train (classify, mandatory data sources):**
```python
{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "train.use_distributed_sampler": False,
    "train.sync_batchnorm": False,
    "dataset.classify.train_dataset.images_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.classify.train_dataset.csv_path": f"{S3_TRAIN}/dataset.csv",
    "dataset.classify.validation_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.classify.validation_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
}
```

**train (segment, mandatory data sources):**
```python
{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "train.use_distributed_sampler": False,
    "train.sync_batchnorm": False,
    "dataset.segment.root_dir": f"{S3_TRAIN}",
}
```

**export (classify):**
```python
{
    "export.input_height": 896,
    "export.input_width": 224,
}
```

**export (segment):**
```python
{
    "export.input_height": 224,
    "export.input_width": 224,
}
```

**quantize (classify, mandatory data sources):**
```python
{
    "dataset.classify.train_dataset.images_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.classify.train_dataset.csv_path": f"{S3_TRAIN}/dataset.csv",
    "dataset.classify.validation_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.classify.validation_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
    "dataset.classify.quant_calibration_dataset.images_dir": f"{S3_TRAIN}/images.tar.gz",
}
```

**evaluate (classify, mandatory data sources):**
```python
{
    "dataset.classify.validation_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.classify.validation_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
    "dataset.classify.test_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.classify.test_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
}
```

**inference (classify, mandatory data sources):**
```python
{
    "dataset.classify.infer_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.classify.infer_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
}
```

**gen_trt_engine (classify, mandatory data sources):**
```python
{
    "gen_trt_engine.tensorrt.calibration.cal_image_dir": [f"{S3_TRAIN}/images.tar.gz"],
}
```

**quantize (segment, mandatory data sources):**
```python
{
    "dataset.segment.root_dir": f"{S3_TRAIN}",
    "dataset.segment.quant_calibration_dataset.images_dir": f"{S3_TRAIN}",
}
```

**evaluate (segment, mandatory data sources):**
```python
{
    "dataset.segment.root_dir": f"{S3_TRAIN}",
}
```

**inference (segment, mandatory data sources):**
```python
{
    "dataset.segment.root_dir": f"{S3_TRAIN}",
}
```

**gen_trt_engine (segment, mandatory data sources):**
```python
{
    "dataset.segment.root_dir": f"{S3_TRAIN}",
    "gen_trt_engine.tensorrt.calibration.cal_image_dir": [f"{S3_TRAIN}/images.tar.gz"],
}
```
## Optional: running via the TAO SDK

When running without the TAO SDK (local docker), resolve the TAO pyt image from `versions.yaml` and invoke `visual_changenet <train|evaluate|inference|export|quantize>` directly. `--shm-size=8g` is required, the C-RADIO `.safetensors` must be mounted to `/data/pretrained_models/C-RADIOv2_B.safetensors`, and checkpoint/results_dir can be overridden on the command line. See `references/local-docker.md` for the full `docker run` command, mounts, and overrides.

## Tasks

### Classify (default)

Uses actions: `train`, `evaluate`, `inference`. Defaults template: `references/spec_template_train.yaml`.

### Segment

Uses skill action names `segment_train`, `segment_evaluate`, and
`segment_inference`. When invoking local Docker directly, run TAO CLI subcommands
`train`, `evaluate`, and `inference` with `task: segment` in the spec. The
schema-driven action templates are `references/spec_template_segment_train.yaml`,
`references/spec_template_segment_evaluate.yaml`, and
`references/spec_template_segment_inference.yaml`; the compact direct-Docker
example template is `references/spec_template_segment.yaml`.

Segmentation requires compiling custom CUDA ops (`MultiScaleDeformableAttention`) on first run, which takes ~5 minutes. The ViT adapter backbone uses these for multi-scale feature extraction.

Dataset structure for segmentation differs from classify — uses paired directories (`A/`, `B/`, `list/`, `label/`) instead of CSV files. See `dataset.segment.root_dir` in the defaults.

## Data Format

Classify needs a 4-column CSV (`input_path,golden_path,label,object_name`) plus an images directory; segment uses a paired directory structure (`A/`, `B/`, `list/`, `label/`) under `dataset.segment.root_dir` instead of CSV. The `image_ext` field (default `.jpg`) must match the actual file extensions; if images are `.png`, set `dataset.classify.image_ext: .png`. Multi-lighting input is configured via `dataset.classify.input_map` (each lighting name maps to a channel index) with `dataset.classify.num_input` set to match. See `references/data-formats.md` for the per-field input tables (classify train/eval/inference, segment), CSV column semantics, lighting/path-concatenation conventions, the segment directory layout, and `input_map`/`grid_map` examples.

## Important Parameters

Key knobs include `train.validation_interval` (default 50, must be ≤ num_epochs), `train.checkpoint_interval` (default 200, must be ≤ num_epochs), `train.num_epochs` (default 100), `model.classify.eval_margin` (default 0.3, the precision/recall threshold), `model.classify.train_margin_euclid` (default 2.0), `model.classify.embedding_vectors` (default 5), `dataset.classify.batch_size` (default 16, must be > 1), `dataset.classify.fpratio_sampling` (default 0.25), and `train.classify.cls_weight` (default [1.0, 10.0]). Hardware: minimum 1 GPU with 16GB+ VRAM, recommended 8 GPUs (DDP); do not set `gpu_spec_key` (GPU count is managed internally by TAO), `num_nodes` (default 1) controls multi-node. See `references/tuning-parameters.md` for the full per-parameter guidance and hardware detail.

## Error Patterns

For checkpoint-not-found, CSV format mismatch, image extension mismatch, OOM, low evaluation accuracy, the contrastive-loss `AssertionError`, checkpoint load key mismatch at evaluate/inference, non-convergence, segment-only backbone dimension mismatch, the `MultiScaleDeformableAttention` `OSError`, the Lightning `MisconfigurationException`, `ModuleNotFoundError: nvidia_tao_pytorch`, and epoch defaults, see `references/troubleshooting.md` for the full symptom-and-fix list.

## Spec Param / Parent Model Inference

Model-specific parent-model mappings are declared in `references/skill_info.yaml` under `spec_params`, so generated runners and agents resolve checkpoints before `create_job()` instead of guessing file names. For `parent_model` or `parent_model_folder`, pass the upstream train/export/AutoML child job id as `parent_job_id`; the SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. See `references/parent-model-inference.md` for the full per-action spec-field-to-inference-function mapping table.

## Deployment

- [tao-deploy-visual-changenet](references/tao-deploy-visual-changenet.md)