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

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 — Visual ChangeNet deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.

tao-train-visual-changenet

NPX Install

Tags

SKILL.md Content