Tao Train Segformer

SegFormer

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 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.

Training Requirements

• Dataset type: segmentation
• Formats: unet
• Monitoring metric: val_miou

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.

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

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": f"{S3_TRAIN}",
}

evaluate (mandatory data sources):

{
    "evaluate.batch_size": 4,
    "dataset.segment.root_dir": f"{S3_EVAL}",
}

gen_trt_engine:

{
    "gen_trt_engine.tensorrt.data_type": "fp16",
}

inference (mandatory data sources):

{
    "dataset.segment.batch_size": 1,
    "dataset.segment.root_dir": f"{S3_EVAL}",
}

export (mandatory data sources):

{
    "dataset.segment.root_dir": f"{S3_TRAIN}",
}

quantize (mandatory data sources):

{
    "dataset.segment.root_dir": f"{S3_TRAIN}",
    "dataset.segment.quant_calibration_dataset.images_dir": f"{S3_TRAIN}",
}

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.

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.

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