SegFormer

SegFormer

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction. Efficient for real-time segmentation tasks.

Set model.backbone.pretrained_backbone_path for backbone weights.

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/​tao-deploy-segformer.md first. 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.

Supported Actions

The packaged SegFormer PyT CLI supports train, evaluate, export, inference, quantize, and default_specs. This model skill exposes train, evaluate, export, inference, and quantize; resume/​retrain is performed through train with train.resume_training_checkpoint_path.

The parent PyT CLI does not expose gen_trt_engine. Use models/​segformer/​deploy for TensorRT engine generation, TensorRT evaluation, and TensorRT inference.

Training Requirements

• Dataset type: segmentation
• Formats: unet
• AutoML training metric: val_miou (maximize).
• Standalone evaluation metric: test_miou. Use the train-status val_miou for AutoML ranking and the evaluator's test_miou only for checkpoint verification.

Per-Action Dataset Requirements

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.

SEG_TRAIN_ROOT = "/​data/​segformer/​train"
SEG_EVAL_ROOT = "/​data/​segformer/​eval"
SEG_INFER_ROOT = "/​data/​segformer/​infer"
CAL_IMAGES = f"{SEG_TRAIN_ROOT}/​images/​train"

train (mandatory data sources):

{
    "train.num_gpus": 1,
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "dataset.segment.batch_size": 4,
    "dataset.segment.root_dir": SEG_TRAIN_ROOT,
}

evaluate (mandatory data sources):

{
    "evaluate.batch_size": 4,
    "dataset.segment.root_dir": SEG_EVAL_ROOT,
    "evaluate.checkpoint": CHECKPOINT,
}

inference (mandatory data sources):

{
    "dataset.segment.batch_size": 1,
    "dataset.segment.root_dir": SEG_INFER_ROOT,
    "inference.checkpoint": CHECKPOINT,
}

export (mandatory data sources):

{
    "dataset.segment.root_dir": SEG_TRAIN_ROOT,
    "export.checkpoint": CHECKPOINT,
    "export.input_height": 256,
    "export.input_width": 256,
    "export.onnx_file": ONNX_FILE,
}

quantize (mandatory data sources):

{
    "dataset.segment.root_dir": SEG_TRAIN_ROOT,
    "dataset.segment.quant_calibration_dataset.images_dir": CAL_IMAGES,
    "quantize.model_path": CHECKPOINT,
}

If the source dataset is delivered as separate images/​*.tar.gz and masks/​*.tar.gz archives, extract them before launch so root_dir contains directories such as images/​train, images/​val, images/​test, masks/​train, and masks/​val. Do not point dataset.segment.root_dir at an archive staging folder that still contains only tarballs.

Eval Dataset

Optional. Validation data is typically part of the root_dir structure.

Important Parameters

• dataset.segment.num_classes: Number of segmentation classes. Default 2 (binary). Must match the number of classes in your mask annotations.
• model.backbone.type: Default fan_small_12_p4_hybrid. Supported includes FAN variants, SegFormer MIT variants, and others.
• dataset.segment.root_dir: Root directory of the segmentation dataset.
• dataset.segment.img_size: Input image size. Default 256. Increase for finer segmentation at the cost of memory.
• train.optim.lr: Learning rate. Default 6e-5.
• model.freeze_backbone: Whether to freeze the backbone during training. Useful for fine-tuning with limited data.
• dataset.segment.batch_size: Per-GPU batch size. Default 8.
• dataset.segment.label_transform: Use the string "None" when no label transform is desired. Do not set this to JSON/​YAML null; strict schema merge treats the field as a string enum.
• dataset.segment.palette: For grayscale masks, use one integer per RGB entry, for example rgb: [85]. Preserve the dataset's actual label ids and class names rather than normalizing them unless the user explicitly asks for a conversion.

Multi-GPU / Multi-Node

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

• Multi-GPU strategy: ddp_find_unused_parameters_true
• No fsdp support

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

Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. SegFormer is relatively lightweight. Default img_size=256 is memory-friendly. Increase img_size for higher resolution at the cost of memory and speed.

Error Patterns

CUDA out of memory: Reduce batch_size or img_size. SegFormer memory scales quadratically with image size.

num_classes mismatch: Ensure dataset.segment.num_classes matches the actual number of classes in your mask annotations.

TensorBoard unsupported for segmentation training: Keep train.tensorboard.enabled: false. The SegFormer training entrypoint asserts that TensorBoard visualization is not supported for segmentation, so do not enable TensorBoard just to extract AutoML metrics; use log parsing or a post-train evaluator instead.

AutoML metric extraction: SegFormer train status files report val_miou alongside val_loss, val_acc, and other validation KPIs. Default AutoML train launches must optimize val_miou with direction: maximize; do not optimize val_loss for default model invocations.

For AutoML or long segmentation sweeps, read val_miou from results_dir/​train/​status.json first. If the wrapper reports a terminal failure but the structured status file reached the configured training budget and contains finite val_miou, report the recovered metric with the wrapper failure noted instead of discarding the measurement.

For high-resolution custom segmentation targets, keep dataset paths as per-run inputs. Do not add customer/​user-specific roots to this reusable skill. When the user asks for a fixed full-budget search, remember that bracket algorithms (asha, bohb, dehb, hyperband, hyperband_es, pbt) may intentionally lower train.num_epochs for some recommendations; use Bayesian/​BFBO or lock the budget if every recommendation must run the full epoch count.

Checkpoint handoff: For evaluate/​export/​inference/​quantize/​resume, use the checkpoint resolver on the best AutoML child job's results_dir/​train/ folder and select the action-appropriate model_epoch_*.pth checkpoint, such as model_epoch_000_step_00010.pth. SegFormer may also write segformer_model_latest.pth, but that should only be used when a caller explicitly requests latest. Preserve dataset.segment.num_classes, dataset.segment.img_size, and dataset.segment.root_dir overrides for downstream actions.

Resume/​retrain checkpoint: Resume uses train.resume_training_checkpoint_path. Pass the exact resolved checkpoint from the previous train output, not a guessed model.pth path. A resumed one-epoch run should produce the next checkpoint in the new results directory, for example model_epoch_001_step_00020.pth.

Export / TensorRT shape alignment: Keep export.input_height and export.input_width aligned with dataset.segment.img_size unless the trained model and deploy specs have been validated at another resolution. The packaged fresh-install path is validated at 256x256, matching the default SegFormer dataset and deploy templates.

Parent segformer gen_trt_engine rejected by the PyT CLI: In the validated 7.0.0 PyT container, segformer gen_trt_engine is not a valid parent-model subtask. Use the SegFormer deploy workflow (references/​tao-deploy-segformer.md) for TensorRT engine generation, TensorRT evaluation, and TensorRT inference.

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 segformer.config.json:

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-segformer