Sparse4D

Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable attention across camera views and time for end-to-end 3D perception. Includes instance bank for temporal tracking.

Requires pretrained ResNet-101 backbone. Set train.pretrained_model_path.

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: sparse4d
Formats: ovpkl
Monitoring metric: val_mAP

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
dataset_convert	aicity.root	id		No
evaluate	dataset.data_root	eval_dataset	(from convert job, spec: aicity.split)	No
evaluate	model.head.instance_bank.anchor	train_datasets	/results/{dataset_convert_job_id}/anchor_init.npy	No
evaluate	dataset.train_dataset.ann_file	train_datasets	(from convert job, spec: aicity.split)	No
evaluate	dataset.val_dataset.ann_file	eval_dataset	(from convert job, spec: aicity.split)	No
evaluate	dataset.test_dataset.ann_file	inference_dataset	(from convert job, spec: aicity.split)	No
export	model.head.instance_bank.anchor	train_datasets	/results/{dataset_convert_job_id}/anchor_init.npy	No
inference	dataset.data_root	inference_dataset	(from convert job, spec: aicity.split)	No
inference	model.head.instance_bank.anchor	train_datasets	/results/{dataset_convert_job_id}/anchor_init.npy	No
inference	dataset.train_dataset.ann_file	train_datasets	(from convert job, spec: aicity.split)	No
inference	dataset.val_dataset.ann_file	eval_dataset	(from convert job, spec: aicity.split)	No
inference	dataset.test_dataset.ann_file	inference_dataset	(from convert job, spec: aicity.split)	No
quantize	dataset.data_root	train_datasets	(from convert job, spec: aicity.split)	No
quantize	model.head.instance_bank.anchor	train_datasets	/results/{dataset_convert_job_id}/anchor_init.npy	No
quantize	dataset.train_dataset.ann_file	train_datasets	(from convert job, spec: aicity.split)	No
quantize	dataset.val_dataset.ann_file	eval_dataset	(from convert job, spec: aicity.split)	No
quantize	dataset.test_dataset.ann_file	inference_dataset	(from convert job, spec: aicity.split)	No
quantize	dataset.quant_calibration_dataset.images_dir	train_datasets		No
train	dataset.data_root	train_datasets	(from convert job, spec: aicity.split)	No
train	model.head.instance_bank.anchor	train_datasets	/results/{dataset_convert_job_id}/anchor_init.npy	No
train	dataset.train_dataset.ann_file	train_datasets	(from convert job, spec: aicity.split)	No
train	dataset.val_dataset.ann_file	eval_dataset	(from convert job, spec: aicity.split)	No
train	dataset.test_dataset.ann_file	inference_dataset	(from convert job, spec: aicity.split)	No

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

python

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

train (mandatory data sources):

python

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.sequences.split_num": 90,
    "train_dataset.sequences_split_num": 90,
    "dataset.data_root": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
}

evaluate (mandatory data sources):

python

{
    "dataset.data_root": {"spec": f"{S3_EVAL}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
}

export (mandatory data sources):

python

{
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
}

inference (mandatory data sources):

python

{
    "dataset.data_root": {"spec": f"{S3_EVAL}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
}

quantize (mandatory data sources):

python

{
    "dataset.data_root": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.quant_calibration_dataset.images_dir": f"{S3_TRAIN}",
}

Eval Dataset

Optional. Val/test splits configured via dataset ann_file paths.

Important Parameters

model.backbone: Backbone. Default resnet_101.
model.neck.out_channels: FPN output channels. Default 256. num_outs=4.
model.input_shape: Input image shape [W, H]. Default [1408, 512].
model.head.num_output: Number of detection output queries. Default 300.
model.head.num_decoder: Number of decoder layers. Default 6.
model.head.temporal: Enable temporal reasoning. Default True.
model.head.instance_bank.num_anchor: Instance bank anchors. Default 900.
model.head.instance_bank.num_temp_instances: Temporal instance count. Default 600.
model.depth_branch.loss_weight: Depth supervision loss weight. Default 0.2.
dataset.batch_size: Per-GPU batch size. Default 2.
dataset.num_frames: Sequence length. Default 200.
dataset.classes: Detection classes. Default [person, gr1_t2, agility_digit, nova_carter]. num_ids=70 for tracking.
train.optim.lr: Learning rate. Default 5e-5. img_backbone lr_mult=0.2.
train.lr_scheduler: Cosine scheduler with linear warmup (500 iters, ratio 0.333).
train.grad_clip.max_norm: Gradient clipping. Default 25.
train.precision: Options: bf16, fp16, fp32. Default bf16.
evaluate.metrics: Eval metrics. Default ["detection"]. Optional tracking evaluation.
evaluate.tracking.enabled: Enable tracking evaluation. tracking_threshold=0.2.

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]
`train.num_nodes`	Number of nodes	1

Multi-GPU strategy:
```
ddp_find_unused_parameters_true
```
(no fsdp support)
```
sync_batchnorm
```
is always enabled (True)

Iterations per epoch computed as:

num_frames * num_bev_groups / (num_nodes * num_gpus * batch_size)

Scaling: When increasing GPUs, effective batch size grows and iterations-per-epoch shrinks proportionally

Multi-node env vars (set by orchestrator):

WORLD_SIZE

NODE_RANK

MASTER_ADDR

MASTER_PORT

NUM_GPU_PER_NODE

Hardware

Minimum 2 GPU(s), recommended 8 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. Multi-camera temporal model is memory intensive. bf16 required for practical training. Multi-GPU strongly recommended. Instance bank requires substantial memory for temporal reasoning.

Error Patterns

dataset_convert required: Must run dataset_convert first to produce annotation pickles and anchor_init.npy.

Missing anchor file: Set model.head.instance_bank.anchor to the anchor_init.npy path from dataset_convert results.

Temporal OOM: Reduce dataset.num_frames or dataset.batch_size if running out of memory during temporal training.

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

sparse4d.config.json

Action	Spec Field	Inference Function	Meaning
dataset_convert	`results_dir`	`output_dir`	current job results directory
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	`results_dir`	`output_dir`	current job results directory
quantize	`encryption_key`	`key`	encryption key
quantize	`quantize.model_path`	`parent_model`	model file inferred from the parent job results folder
quantize	`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

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.

tao-train-sparse4d

NPX Install

Tags

SKILL.md Content

Sparse4D

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Hardware

Error Patterns

Spec Param / Parent Model Inference