Tao Train Optical Inspection
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
- Has scripts
From the skill’s files
Taken from the skill’s own files (its description, section headings and stated usage). Not an AutoGineer summary, and not written by the author as one.
Optical Inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing defects, anomalies, or quality issues. Use when training, evaluating, exporting, or running inference for a TAO Optical Inspection model on AOI / quality-control data. Trigger phrases include "train optical inspection", "AOI defect detection", "Siamese defect classifier", "PCB / manufacturing inspection".
What it does
- Dataclass Schemas
- Train Action Policy
- Training Requirements
- dataset.csv Contract
- Dataset Convert
- Eval Dataset
When to use it
- Use when training, evaluating, exporting, or running inference for a TAO Optical Inspection model on AOI / quality-control data.
- Trigger phrases include "train optical inspection", "AOI defect detection", "Siamese defect classifier", "PCB / manufacturing inspection".
Advantages
The skill’s files do not state this.
Usage examples
The skill’s files do not state this.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/tao-train-optical-inspection/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-train-optical-inspection.zip -d .agents/skills
Documentation by NVIDIA Corporation
Optical Inspection
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
Optical inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing defects, anomalies, or quality issues.
Set train.pretrained_model_path for pretrained Siamese weights.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-optical-inspection.md first. The parent PyT container does not expose optical_inspection gen_trt_engine; TensorRT engine generation is deploy-only. Deploy spec templates live in this skill's references/ folder with the spec_template_deploy_*.yaml prefix.
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 for an action requires schemas/<action>.schema.json and references/spec_template_<action>.yaml to exist and parse. Use the packaged selected-action 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 such as evaluate, inference, export, and deploy flows stay in this model skill. The per-run automl_policy override does not change model metadata.
Training Requirements
Dataset type: optical_inspection
Formats: default
AutoML training metric:
val_acc, withdirection=maximizeStandalone evaluation metric:
test_acc, withdirection=maximize
Per-Action Dataset Requirements
| Action | Spec Key | Source | Files | List? |
|---|---|---|---|---|
| evaluate | dataset.test_dataset.images_dir | eval_dataset | images.tar.gz | No |
| evaluate | dataset.test_dataset.csv_path | eval_dataset | dataset.csv | No |
| inference | dataset.infer_dataset.images_dir | inference_dataset | images.tar.gz | No |
| inference | dataset.infer_dataset.csv_path | inference_dataset | dataset.csv | No |
| train | dataset.train_dataset.images_dir | train_datasets | images.tar.gz | No |
| train | dataset.train_dataset.csv_path | train_datasets | dataset.csv | No |
| train | dataset.validation_dataset.images_dir | eval_dataset | images.tar.gz | No |
| train | dataset.validation_dataset.csv_path | eval_dataset | dataset.csv | No |
| train | dataset.test_dataset.images_dir | eval_dataset | images.tar.gz | No |
| train | dataset.test_dataset.csv_path | eval_dataset | dataset.csv | No |
images.tar.gz is the transfer artifact. After staging/extraction,
dataset.*_dataset.images_dir must point at the inner directory that directly
contains golden/ and the board directories used by the CSV.
dataset.csv Contract
The Optical Inspection loader reads the CSV by column name and constructs both sides of every Siamese comparison as:
<images_dir>/<input_path>/<object_name>_<lighting><image_ext>
<images_dir>/<golden_path>/<object_name>_<lighting><image_ext>Absolute input_path and golden_path values are also accepted by the loader;
an absolute value replaces images_dir. Relative directories are recommended
because they remain portable after staging. Do not put a filename in either
path column and do not put a lighting suffix or extension in object_name.
| Column | Type | Required | Allowed values and meaning |
|---|---|---|---|
input_path | string directory path | yes | Board/capture component directory, relative to images_dir or absolute. |
golden_path | string directory path | yes | Golden/reference component directory, relative to images_dir or absolute. |
label | string | yes | Exact case-sensitive PASS means non-defective (class 0). Any other non-empty defect name means defective (class 1), for example missing, shift, excess_solder, lifted_lead, polarity, tombstone, or upside down. pass is invalid because the loader would silently treat it as a defect. |
object_name | string filename stem | yes | Component identifier such as C1018@1; no directory, _SolderLight, or .jpg. The loader forces this column to string so numeric-looking identifiers retain their text form. |
Additional metadata columns are allowed but ignored by this loader. Empty values are invalid. One complete row, grounded in the production layout, is:
input_path,golden_path,label,object_name
690-5G190-0510-001P1/AOI_B/FXLH_690-5G190-0510-001P1_30332_P_AOI_B_20230317130332/PerComponent,golden/images/690-5G190-0510-001P1BOT/,PASS,C1018@1With the standard four-light configuration, each row requires eight files:
<images_dir>/
├── golden/images/690-2G133-0210-000BOT/
│ ├── R821@1_LowAngleLight.jpg
│ ├── R821@1_SolderLight.jpg
│ ├── R821@1_UniformLight.jpg
│ └── R821@1_WhiteLight.jpg
└── 690-2G133-0210-000/AOI_B/<capture>/PerComponent/
├── R821@1_LowAngleLight.jpg
├── R821@1_SolderLight.jpg
├── R821@1_UniformLight.jpg
└── R821@1_WhiteLight.jpgThe three selection fields must agree:
input_mapkeys are the exact filename lighting suffixes. Current loader behavior iterates the YAML key insertion order; it does not sort by the integer values. Keep values contiguous and in matching order (0..N-1).num_inputmust equal the number ofinput_mapentries. The loader opens every key, so a mismatched value does not limit the file list and produces a tensor/export shape mismatch.concat_type: linearstacks inputs in key order along image height. With four 128×128 inputs this produces a 512×128 tensor;grid_mapis ignored.concat_type: gridrequires an evennum_inputandgrid_map.x * grid_map.y == num_input. Placement is row-major in key order. For the standard2 x 2map: LowAngle is upper-left, Solder upper-right, Uniform lower-left, and White lower-right.
If the dataset genuinely contains only *_SolderLight.jpg, use
num_input: 1, input_map: {SolderLight: 0}, and concat_type: linear. Do not
declare four inputs when three variants are absent.
Run the packaged preflight before train, evaluate, or inference, using the same dataset settings as the spec:
python3 skills/models/tao-train-optical-inspection/scripts/validate_dataset.py \
--csv /data/optical-inspection/train/dataset.csv \
--images-dir /data/optical-inspection/train/images \
--num-input 4 \
--concat-type grid \
--grid-x 2 --grid-y 2For a custom map, repeat --input-map LIGHT=INDEX in YAML key order and pass
the spec's --image-ext. The validator reports missing columns, unsafe PASS
case, unresolvable row directories, and every missing lighting file with its
CSV row number. A two-row path-stub fixture is under
tests/fixtures/dataset/valid/; it verifies the contract and is not PCB
training data.
No published Optical Inspection sample dataset is discoverable from this skill bank. Obtain converted AOI data from the dataset owner for your product or organization, then stage it at the paths mounted into the container. Do not use the committed validator fixture for model training.
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.
TRAIN_ROOT = "/data/optical-inspection/train"
EVAL_ROOT = "/data/optical-inspection/eval"
INFERENCE_ROOT = "/data/optical-inspection/inference"These are example in-container mount points after obtaining and staging data from its owner; they are not download locations.
train (mandatory data sources):
{
"train.num_epochs": 30,
"train.checkpoint_interval": 10,
"train.validation_interval": 10,
"train.num_gpus": 1,
"dataset.batch_size": 8,
"dataset.train_dataset.images_dir": f"{TRAIN_ROOT}/images",
"dataset.train_dataset.csv_path": f"{TRAIN_ROOT}/dataset.csv",
"dataset.validation_dataset.images_dir": f"{EVAL_ROOT}/images",
"dataset.validation_dataset.csv_path": f"{EVAL_ROOT}/dataset.csv",
"dataset.test_dataset.images_dir": f"{EVAL_ROOT}/images",
"dataset.test_dataset.csv_path": f"{EVAL_ROOT}/dataset.csv",
}evaluate (mandatory data sources):
{
"evaluate.checkpoint": "<selected train/AutoML checkpoint>",
"dataset.test_dataset.images_dir": f"{EVAL_ROOT}/images",
"dataset.test_dataset.csv_path": f"{EVAL_ROOT}/dataset.csv",
}Use the workflow's checkpoint resolver for downstream actions instead of guessing a filename. For Optical Inspection smoke runs, AutoML may produce model_epoch_000_step_00006.pth; resume can then produce model_epoch_001_step_00012.pth. Best-checkpoint actions should use the AutoML best child job's selected checkpoint, epoch-specific actions should pass the exact epoch/step checkpoint requested, and only explicit "latest" requests should resolve to the latest checkpoint.
export:
{
"export.checkpoint": "<selected train/AutoML checkpoint>",
"export.onnx_file": "/results/optical_inspection.onnx",
"export.input_width": 128,
"export.input_height": 512,
"export.batch_size": 1,
}inference (mandatory data sources):
{
"inference.checkpoint": "<selected train/AutoML checkpoint>",
"dataset.infer_dataset.images_dir": f"{INFERENCE_ROOT}/images",
"dataset.infer_dataset.csv_path": f"{INFERENCE_ROOT}/dataset.csv",
}Dataset Convert
Dataset conversion is optional for Optical Inspection. If the dataset is already in TAO-ready Optical Inspection format, start directly from the images.tar.gz plus dataset.csv splits and run train, evaluate, inference, and downstream checkpoint/export/deploy actions on that converted data.
The PyT container exposes optical_inspection dataset_convert, but this model skill does not package a dataset_convert action/template. The converter expects the raw Factory PCB layout (root_dataset_dir, train/val/all PCB directories, golden_csv_dir, project_name, and bot_top). Data owners may instead provide preconverted Optical Inspection images.tar.gz plus dataset.csv splits without the raw PCB/golden CSV source. Do not synthesize a fake PCB dataset. In model validation reports, mark dataset conversion as not run: preconverted dataset provided rather than failed or blocked when only converted data is available.
When using preconverted transfer archives locally, verify the extracted directory before writing specs. The archives may unpack an images/ wrapper directory; point dataset.*.images_dir at the inner directory that contains golden/ and the board/image folders referenced by dataset.csv, for example .../<split>/images/images, not the outer wrapper.
Product-side follow-ups remain: publish a licensed, trainable sample dataset and
package the existing container dataset_convert entrypoint as a skill action
with its own schema/template. Neither is implemented by this documentation and
preflight change.
Eval Dataset
Optional. Eval dataset uses same format (images + CSV).
Important Parameters
model.model_type: Siamese variant. Options include Siamese, Siamese_3.
model.model_backbone: Default custom.
model.embedding_vectors: Number of embedding dimensions. Default 5.
train.optim.lr: Learning rate. Default 5e-4.
dataset.batch_size: Training batch size. Must be greater than 1; use
2or higher for minimal smoke runs.dataset.num_input: Number of input images per comparison.
dataset.input_map: Mapping of input channels / image pairs.
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] |
Strategy:
auto(Lightning picks best strategy automatically)No explicit
num_nodesordistributed_strategyconfig — single-node onlyLightweight Siamese network, single GPU typically sufficient
Hardware
Minimum 1 GPU(s), recommended 1 GPU(s). 8GB+ VRAM per GPU. Siamese networks for inspection are lightweight. Single GPU sufficient.
Error Patterns
CSV format error: Require input_path,golden_path,label,object_name, then
run scripts/validate_dataset.py with the spec's images_dir, lighting map,
num_input, concatenation, grid, and image extension before launch.
Extracted image root mismatch: If train, evaluate, or inference cannot find paths from dataset.csv, inspect the extracted images.tar.gz tree. The TAO-ready root must contain golden/ plus the board folders referenced in the CSV. For transferred archives this can be one level below the extraction target, such as images/images.
Training batch size assertion: The Optical Inspection dataloader rejects
dataset.batch_size: 1 for train. Keep the template default of 8 for normal
runs, or set dataset.batch_size: 2 for minimal AutoML smoke validation.
PyTorch checkpoint load failure on downstream actions: For checkpoints
produced by the same trusted TAO train/AutoML workflow, set
TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 for evaluate, inference, export, and
resume jobs if the current PyTorch default blocks loading the full checkpoint.
Do not use this env var for untrusted checkpoints.
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 TAO Core optical_inspection.config.json:
| Action | Spec Field | Inference Function | Meaning |
|---|---|---|---|
| evaluate | encryption_key | key | encryption key |
| evaluate | evaluate.checkpoint | parent_model | model file inferred from the parent job results folder |
| evaluate | results_dir | output_dir | current job results directory |
| export | encryption_key | key | encryption key |
| export | export.checkpoint | parent_model | model file inferred from the parent job results folder |
| export | export.onnx_file | create_onnx_file | output ONNX path |
| export | results_dir | output_dir | current job results directory |
| inference | encryption_key | key | encryption key |
| inference | inference.checkpoint | parent_model | model file inferred from the parent job results folder |
| inference | inference.trt_engine | parent_model | model file inferred from the parent job results folder |
| inference | results_dir | output_dir | current job results directory |
| train | encryption_key | key | encryption key |
| train | results_dir | output_dir | current job results directory |
| train | train.pretrained_model_path | ptm_if_no_resume_model | PTM when no resume checkpoint exists |
| 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
Files
- ATTRIBUTION.mdAdded by AutoGineer954 B
- BENCHMARK.md7.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md17.4 KiB
- config/skillspector-baseline.yaml1.0 KiB
- evals/evals.json839 B
- references/skill_info.yaml4.9 KiB
- references/spec_template_deploy_experiment.yaml1.5 KiB
- references/spec_template_evaluate.yaml2.0 KiB
- references/spec_template_export.yaml2.1 KiB
- references/spec_template_gen_trt_engine.yaml2.3 KiB
- references/spec_template_inference.yaml2.0 KiB
- references/spec_template_train.yaml1.9 KiB
- references/tao-deploy-optical-inspection.md5.6 KiB
- references/tao-deploy-optical-inspection.skill_info.yaml3.5 KiB
- schemas/evaluate.schema.json33.2 KiB
- schemas/export.schema.json33.2 KiB
- schemas/gen_trt_engine.schema.json38.6 KiB
- schemas/inference.schema.json33.2 KiB
- schemas/manifest.json15.5 KiB
- schemas/train.schema.json33.6 KiB
- scripts/validate_dataset.pyScript11.2 KiB
- skill-card.md4.4 KiB
- skill.oms.sig18.5 KiB
- tests/fixtures/dataset/invalid_label.csv189 B
- tests/fixtures/dataset/missing_columns.csv91 B
- tests/fixtures/dataset/missing_lighting/dataset.csv109 B
- tests/fixtures/dataset/missing_lighting/images/board/AOI_B/capture/PerComponent/C42@1_LowAngleLight.jpg0 B
- tests/fixtures/dataset/missing_lighting/images/board/AOI_B/capture/PerComponent/C42@1_SolderLight.jpg0 B
- tests/fixtures/dataset/missing_lighting/images/board/AOI_B/capture/PerComponent/C42@1_UniformLight.jpg0 B
- tests/fixtures/dataset/missing_lighting/images/golden/images/boardBOT/C42@1_LowAngleLight.jpg0 B
- tests/fixtures/dataset/missing_lighting/images/golden/images/boardBOT/C42@1_SolderLight.jpg0 B
- tests/fixtures/dataset/missing_lighting/images/golden/images/boardBOT/C42@1_UniformLight.jpg0 B
- tests/fixtures/dataset/missing_lighting/images/golden/images/boardBOT/C42@1_WhiteLight.jpg0 B
- tests/fixtures/dataset/missing_path.csv105 B
- tests/fixtures/dataset/single_class.csv338 B
- tests/fixtures/dataset/valid/dataset.csv341 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_B/FXLH_690-2G133-0210-000_1320422122195_F_AOI_B_20220123100122/PerComponent/R821@1_LowAngleLight.jpg0 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_B/FXLH_690-2G133-0210-000_1320422122195_F_AOI_B_20220123100122/PerComponent/R821@1_SolderLight.jpg0 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_B/FXLH_690-2G133-0210-000_1320422122195_F_AOI_B_20220123100122/PerComponent/R821@1_UniformLight.jpg0 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_B/FXLH_690-2G133-0210-000_1320422122195_F_AOI_B_20220123100122/PerComponent/R821@1_WhiteLight.jpg0 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_T/FXLH_690-2G133-0210-000_1320422122038_F_AOI_T_20220123130101/PerComponent/R1209@1_LowAngleLight.jpg0 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_T/FXLH_690-2G133-0210-000_1320422122038_F_AOI_T_20220123130101/PerComponent/R1209@1_SolderLight.jpg0 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_T/FXLH_690-2G133-0210-000_1320422122038_F_AOI_T_20220123130101/PerComponent/R1209@1_UniformLight.jpg0 B
- tests/fixtures/dataset/valid/images/690-2G133-0210-000/AOI_T/FXLH_690-2G133-0210-000_1320422122038_F_AOI_T_20220123130101/PerComponent/R1209@1_WhiteLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000BOT/R821@1_LowAngleLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000BOT/R821@1_SolderLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000BOT/R821@1_UniformLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000BOT/R821@1_WhiteLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000TOP/R1209@1_LowAngleLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000TOP/R1209@1_SolderLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000TOP/R1209@1_UniformLight.jpg0 B
- tests/fixtures/dataset/valid/images/golden/images/690-2G133-0210-000TOP/R1209@1_WhiteLight.jpg0 B
- tests/test_validate_dataset.pyScript4.8 KiB
LICENSE
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs Python, Needs Node.js
The author says: Requires docker + nvidia-container-toolkit.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-train-optical-inspection · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-train-optical-inspection.zip