Tao Train Visual Changenet

---
name: tao-train-visual-changenet
description: Visual ChangeNet for binary image classification and segmentation in AOI defect detection. Use when training,
category: development-code
version: 1.0.0
  evaluating, exporting, or running inference for PCB defect detection or visual inspection, comparing image pairs for
  PASS/NO_PASS classification, or producing change-segmentation masks. Trigger phrases include "train Visual ChangeNet",
  "ChangeNet classify", "ChangeNet segment", "AOI defect detection", "PCB inspection model".
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit.
metadata:
  author: NVIDIA Corporation
  version: "0.1.0"
allowed-tools: Read Bash
tags:
- pcb
- aoi
- defect
- classification
- segmentation
- siamese
- visual-inspection
---

# 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.

## 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. 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.

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.

## Tasks

### Classify (default)

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

### Segment

Uses actions: `segment_train`, `segment_evaluate`, `segment_inference`. Defaults template: `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.

## Datasets, Spec Overrides, and Data Format

Visual ChangeNet has two task modes with different dataset types and data source structures. Classify uses 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 a single `root_dir`. Data source overrides are **mandatory for every action** — the agent MUST construct data source paths and include them in `spec_overrides`.

See `references/dataset-and-specs.md` for the full per-action dataset requirement tables (classify and segment), every spec-override example (train, export, quantize, evaluate, inference, gen_trt_engine for both variants), the classify CSV format, evaluate/inference and segment input fields, lighting conventions, segment data layout, and the `input_map` multi-lighting configuration.

## Local Docker Invocation

Without the TAO SDK, resolve the TAO pyt image from `versions.yaml` and invoke `visual_changenet <action>` directly with `--shm-size=8g` and the backbone `.ckpt` mounted as a single file. See `references/local-docker-invocation.md` for the full `docker run` command, the shared-memory requirement, the backbone mount detail, and the checkpoint/results_dir command-line override pattern.

## Parameters, Hardware, and Error Patterns

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 primary precision/recall threshold), and `train.classify.cls_weight` (default [1.0, 10.0]). Minimum hardware is 1 GPU with 16GB+ VRAM; 8 GPUs (DDP) are recommended for production. GPU count is managed internally by TAO — do not set `gpu_spec_key`.

See `references/parameters-and-troubleshooting.md` for the full parameter reference, hardware guidance, and the complete error-pattern catalog (checkpoint not found, CSV format mismatch, image extension mismatch, OOM, low eval accuracy, the contrastive-loss assertion, non-convergence, the segment-only MultiScaleDeformableAttention build, Lightning epoch misconfiguration, PYTHONPATH/ModuleNotFoundError, and epoch defaults).

## Spec Param / Parent Model Inference

Model-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.

Inference mappings from this model skill:

| Action | Spec Field | Inference Function | Meaning |
|---|---|---|---|
| evaluate | `results_dir` | `output_dir` | current job results directory |
| inference | `results_dir` | `output_dir` | current job results directory |
| train | `results_dir` | `output_dir` | current job results directory |
| train | `train.resume_training_checkpoint_path` | `resume_model` | model file inferred from the current job results folder |

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. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.

## Deployment

- [tao-deploy-visual-changenet](references/tao-deploy-visual-changenet.md) — Visual ChangeNet deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.