Sparse4D

Sparse4D is a Multi-Camera 3D Detection and Tracking model with 4D (spatial-temporal) capabilities. It takes synchronized input images from multiple cameras and calibration matrices and outputs the 3D bounding boxes and temporally consistent tracking IDs. The model is based on resnet101, which is a general-purpose backbone for computer vision.

Each batch in Sparse4D is trained on a group of cameras. Each group is called as a bird’s-eye view (BEV) group. A BEV group is a collection of multiple overlapping cameras.

The expected data requirements and time to fine-tune the Sparse4D model on a single scene of the MTMC Tracking 2025 dataset are as follows:

Estimated time for fine-tuning Sparse4D on a single scene of the MTMC Tracking 2025 dataset

Backbone type	GPU type	Image size	No. of BEV groups	No. of cameras in each BEV group	No. of frames in each camera	Total no. of epochs	Total training time
Resnet101	8 x Nvidia H100 - 80GB SXM	3x512x1408	3 (Minimum BEV groups)	4-12	9000 (5 mins @ 30 FPS)	5	10 hours

Sparse4D supports the following tasks:

• train

• evaluate

• inference

• export

• quantize

Data Input for Sparse4D

The Sparse4D apps in TAO utilize the dataset and for its training, validation and testing.

Refer to the MTMC Tracking 2025 dataset page in the PhysicalAI-SmartSpaces HuggingFace repository for more about the raw dataset format. The dataset is converted into pickle format and stored in the data/sparse4d/ directory.

Creating an Experiment Specification File

The specification file for Sparse4D includes model, dataset, train parameters, visualize parameters, evaluate parameters and inference parameters. The following is an example specification file for training a Sparse4D model on one scene of the MTMC Tracking 2025 dataset. We will utilize the Warehouse_014 scene from the MTMC Tracking 2025 dataset for training.

The experiment specification consists of several main components:

• dataset

• model

• train

• evaluate

• inference

• export

• visualize

dataset

The dataset parameter defines the dataset source, training batch size, and augmentation. An example dataset is provided below. This section describes the main parameters of the Omniverse3DDetTrackDatasetConfig.

dataset:
  use_h5_file_for_rgb: false
  use_h5_file_for_depth: true
  num_frames: 9000
  batch_size: 2
  num_bev_groups: 1
  num_workers: 2
  num_ids: 70
  classes: [
    "person",
    "gr1_t2",
    "agility_digit",
    "nova_carter",
  ]
  type: "omniverse_3d_det_track"
  data_root: ???
  train_dataset:
    ann_file: ???
    test_mode: false
    use_valid_flag: true
    with_seq_flag: true
    sequences_split_num: 100
    keep_consistent_seq_aug: true
    same_scene_in_batch: true
  val_dataset:
    ann_file: ???
    test_mode: true
    use_valid_flag: true
    tracking: true
    tracking_threshold: 0.2
  test_dataset:
    ann_file: ???
    test_mode: true
    use_valid_flag: true
    tracking: true
    tracking_threshold: 0.2
  augmentation:
    resize_lim: [0.7, 0.77]
    final_dim: [512, 1408]
    bot_pct_lim: [0.0, 0.0]
    rot_lim: [-5.4, 5.4]
    image_size: [1080, 1920]
    rand_flip: true
    rot3d_range: [-0.3925, 0.3925]
  normalize:
    mean: [123.675, 116.28, 103.53]
    std: [58.395, 57.12, 57.375]
    to_rgb: true
  sequences:
    split_num: 100
    keep_consistent_aug: true
    same_scene_in_batch: true

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Dataset type	omniverse_3d_det_track
batch_size	int	Batch size	2	1	infinity
use_h5_file_for_rgb	bool	Use H5 file	False
use_h5_file_for_depth	bool	Use H5 file	True
num_frames	int	Number of frames	200	1	infinity
num_bev_groups	int	Number of BEV groups	1	1	infinity
data_root	string	Path to data root	???
anno_root	string	Path to annotation root	???
classes	list	Classes to detect	[‘person’, ‘humanoid’, ‘nova_carter’, ‘transporter’, ‘forklift’, ‘box’, ‘pallet’, ‘crate’]				false
num_workers	int	Number of workers	4	0	infinity
num_ids	int	Number of IDs	70	1	infinity
augmentation	collection	Augmentation config					false
normalize	collection	Normalize config					false
sequences	collection	Sequences config					false
train_dataset	collection	Train dataset config					false
val_dataset	collection	Val dataset config					false
test_dataset	collection	Test dataset config					false

Train Dataset Configuration (dataset.train_dataset)

Configuration for the training dataset.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
ann_file	string	Path to annotation file	???
test_mode	bool	Test mode	False
use_valid_flag	bool	Use valid flag	True
with_seq_flag	bool	With sequence flag	True
sequences_split_num	int	Number of sequences	100	1	infinity
keep_consistent_seq_aug	bool	Keep consistent sequence augmentation	True
same_scene_in_batch	bool	Same scene in batch	True

Validation Dataset Configuration (dataset.val_dataset)

Configuration for the validation dataset.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
ann_file	string	Path to annotation pickle files/folders	???
test_mode	bool	Test mode	False
use_valid_flag	bool	Use valid flag	True
tracking	bool	Tracking	True
tracking_threshold	float	Tracking threshold	0.2	0	1
same_scene_in_batch	bool	Same scene in batch	True

Test Dataset Configuration (dataset.test_dataset)

Configuration for the test dataset.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
ann_file	string	Path to annotation pickle files/folders	???
test_mode	bool	Test mode	True
use_valid_flag	bool	Use valid flag	True
tracking	bool	Tracking	True
tracking_threshold	float	Tracking threshold	0.2	0	1
same_scene_in_batch	bool	Same scene in batch	True

Augmentation Configuration (dataset.augmentation)

Configuration for data augmentation.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
resize_lim	list	Resize limits	[0.7, 0.77]				false
final_dim	list	Final dimensions	[512, 1408]				false
bot_pct_lim	list	Bottom percentage limits	[0.0, 0.0]				false
rot_lim	list	Rotation limits in degrees	[-5.4, 5.4]				false
image_size	list	Original image size	[1080, 1920]				false
rand_flip	bool	Random flip	True
rot3d_range	list	3D rotation range in radians	[-0.3925, 0.3925]				false

Normalize Configuration (dataset.normalize)

Configuration for image normalization.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
mean	list	Mean values for normalization	[123.675, 116.28, 103.53]				false
std	list	Standard deviation values for normalization	[58.395, 57.12, 57.375]				false
to_rgb	bool	Convert to RGB	True

Sequences Configuration (dataset.sequences)

Configuration for handling image sequences.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
split_num	int	Number of sequence splits	100	1	infinity
keep_consistent_aug	bool	Keep consistent augmentation	True
same_scene_in_batch	bool	Keep same scene in batch	True

model

The model parameter provides options to change the Sparse4D architecture.

model:
  type: "sparse4d"
  use_grid_mask: true
  use_deformable_func: true
  use_temporal_align: true
  input_shape: [1408, 512]
  embed_dims: 256
  neck:
    type: "FPN"
    num_outs: 4
    start_level: 0
    out_channels: 256
    in_channels: [256, 512, 1024, 2048]
    add_extra_convs: "on_output"
    relu_before_extra_convs: true
  depth_branch:
    type: "dense_depth"
    embed_dims: "${model.embed_dims}"
    num_depth_layers: 3
    loss_weight: 0.2
  head:
    type: "sparse4d"
    num_output: 300
    cls_threshold_to_reg: 0.05
    decouple_attn: true
    return_feature: true
    use_reid_sampling: false
    embed_dims: "${model.embed_dims}"
    num_groups: 8
    num_decoder: 6
    num_single_frame_decoder: 1
    drop_out: 0.1
    temporal: true
    with_quality_estimation: true
    instance_bank:
      num_anchor: 900
      anchor: ???
      num_temp_instances: 600
      confidence_decay: 0.8
      feat_grad: false
      default_time_interval: 0.033333
      embed_dims: "${model.embed_dims}"
      use_temporal_align: "${model.use_temporal_align}"
    anchor_encoder:
      type: 'SparseBox3DEncoder'
      vel_dims: 3
      embed_dims: [128, 32, 32, 64]
      mode: 'cat'
      output_fc: false
      in_loops: 1
      out_loops: 4
    operation_order: [
      "deformable", "ffn", "norm", "refine", "temp_gnn", "gnn", "norm",
      "deformable", "ffn", "norm", "refine", "temp_gnn", "gnn", "norm",
      "deformable", "ffn", "norm", "refine", "temp_gnn", "gnn", "norm",
      "deformable", "ffn", "norm", "refine", "temp_gnn", "gnn", "norm",
      "deformable", "ffn", "norm", "refine", "temp_gnn", "gnn", "norm",
      "deformable", "ffn", "norm", "refine"
    ]
    temp_graph_model:
      type: "MultiheadAttention"
      embed_dims: 512
      num_heads: 8
      batch_first: true
      dropout: 0.1
    graph_model:
      type: "MultiheadAttention"
      embed_dims: "${model.head.temp_graph_model.embed_dims}"
      num_heads: "${model.head.temp_graph_model.num_heads}"
      batch_first: true
      dropout: "${model.head.temp_graph_model.dropout}"
    norm_layer:
      type: "LN"
      normalized_shape: "${model.embed_dims}"
    ffn:
      type: "AsymmetricFFN"
      in_channels: 512
      pre_norm:
        type: "LN"
      embed_dims: 256
      feedforward_channels: 1024
      num_fcs: 2
      ffn_drop: 0.1
      act_cfg:
        type: "ReLU"
        inplace: true
    deformable_model:
      embed_dims: "${model.embed_dims}"
      num_groups: 8
      num_levels: 4
      attn_drop: 0.15
      use_deformable_func: true
      use_camera_embed: false
      residual_mode: "cat"
      kps_generator:
        embed_dims: "${model.embed_dims}"
        num_learnable_pts: 6
        fix_scale:
          - [0, 0, 0]
          - [0.45, 0, 0]
          - [-0.45, 0, 0]
          - [0, 0.45, 0]
          - [0, -0.45, 0]
          - [0, 0, 0.45]
          - [0, 0, -0.45]
    refine_layer:
      type: "SparseBox3DRefinementModule"
      embed_dims: "${model.embed_dims}"
      refine_yaw: true
      with_quality_estimation: true
    sampler:
      num_dn_groups: 5
      num_temp_dn_groups: 3
      dn_noise_scale: [2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
      max_dn_gt: 128
      add_neg_dn: true
      cls_weight: 2.0
      box_weight: 0.25
      reg_weights: [2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0]
      use_temporal_align: "${model.use_temporal_align}"
    visibility_net:
      type: "visibility_net"
      embedding_dim: 256
      hidden_channels: 32
    loss:
      reg:
        type: "sparse_box_3d"
        box_weight: 0.25
        cls_allow_reverse: [5, 6, 7]
      cls:
        type: "focal"
        use_sigmoid: true
        gamma: 2.0
        alpha: 0.25
        loss_weight: 2.0
      id:
        type: "cross_entropy_label_smooth"
        num_ids: "${dataset.num_ids}"
    bnneck:
      type: "bnneck"
      feat_dim: 256
      num_ids: "${dataset.num_ids}"
    decoder:
      type: "SparseBox3DDecoder"
      score_threshold: 0.05
    reg_weights: [2.0, 2.0, 2.0, 1 ,1, 1, 1, 1, 1, 1, 1]

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Model type	sparse4d
embed_dims	int	Embedding dimensions	256	1	infinity
use_grid_mask	bool	Use grid mask	True
use_deformable_func	bool	Use deformable function	True
input_shape	list	Input image shape	[1408, 512]				false
backbone	collection	Backbone config					false
neck	collection	Neck config					false
depth_branch	collection	Depth branch config					false
head	collection	Head config					false
use_temporal_align	bool	Use temporal alignment	False

Backbone Configuration (model.backbone)

Configuration for the model’s backbone network. Currently, only resnet_101 is supported.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Backbone type	resnet_101			resnet_101

Head Configuration (model.head)

Top-level configuration for the detection and tracking head.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Head type	sparse4d
num_output	int	Number of output instances	300	1	infinity
cls_threshold_to_reg	float	Classification threshold for regression	0.05	0	1
decouple_attn	bool	Decouple attention	True
return_feature	bool	Return instance features	True
use_reid_sampling	bool	Use Re-ID sampling	False
embed_dims	int	Embedding dimensions	256	1	infinity
reid_dims	int	Re-ID dimensions	0	0	infinity
num_groups	int	Number of groups	8	1	infinity
num_decoder	int	Number of decoder layers	6	1	infinity
num_single_frame_decoder	int	Number of single-frame decoder layers	1	1	infinity
drop_out	float	Dropout rate	0.1	0	1
temporal	bool	Enable temporal modeling	True
with_quality_estimation	bool	Enable quality estimation	True
operation_order	list	Operation order	[‘deformable’, ‘ffn’, ‘norm’, ‘refine’, ‘temp_gnn’, ‘gnn’, ‘norm’, ‘deformable’, ‘ffn’, ‘norm’, ‘refine’, ‘temp_gnn’, ‘gnn’, ‘norm’, ‘deformable’, ‘ffn’, ‘norm’, ‘refine’, ‘temp_gnn’, ‘gnn’, ‘norm’, ‘deformable’, ‘ffn’, ‘norm’, ‘refine’, ‘temp_gnn’, ‘gnn’, ‘norm’, ‘deformable’, ‘ffn’, ‘norm’, ‘refine’, ‘temp_gnn’, ‘gnn’, ‘norm’, ‘deformable’, ‘ffn’, ‘norm’, ‘refine’]				false
visibility_net	collection	Visibility net config					false
instance_bank	collection	Instance bank config					false
anchor_encoder	collection	Anchor encoder config					false
sampler	collection	Sampler config					false
reg_weights	list	Regression weights	[2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]				false
loss	collection	Loss config					false
bnneck	collection	BN neck config					false
deformable_model	collection	Deformable model config					false
refine_layer	collection	Refine layer config					false
valid_vel_weight	float	Valid velocity weight	-1	-1	infinity
graph_model	collection	Graph model config					false
temp_graph_model	collection	Temp graph model config					false
decoder	collection	Decoder config					false
norm_layer	collection	Norm layer config					false
ffn	collection	FFN config					false

Deformable Model Configuration (model.head.deformable_model)

Configuration for the deformable attention mechanism.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
embed_dims	int	Embedding dimensions	256	1	infinity
num_groups	int	Number of groups	8	1	infinity
num_levels	int	Number of levels	4	1	infinity
attn_drop	float	Attention dropout	0.15	0	1
use_deformable_func	bool	Use deformable function	True
use_camera_embed	bool	Use camera embedding	False
residual_mode	categorical	Residual mode	cat			cat,add
num_cams	int	Number of cameras	6	1	infinity
max_num_cams	int	Maximum number of cameras	20	1	infinity
proj_drop	float	Projection dropout	0.0	0	1
kps_generator	collection	KPS generator config					false

Instance Bank Configuration (model.head.instance_bank)

Configuration for managing object instances over time.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
num_anchor	int	Number of anchors	900	1	infinity
anchor	string	Path to anchor file
num_temp_instances	int	Number of temporal instances	600	0	infinity
confidence_decay	float	Confidence decay factor	0.8	0	1
feat_grad	bool	Enable gradients for features	False
default_time_interval	float	Default time interval	0.033333	0	infinity
embed_dims	int	Embedding dimensions	256	1	infinity
use_temporal_align	bool	Use temporal alignment	False
grid_size	float	Grid size

Anchor Encoder Configuration (model.head.anchor_encoder)

Configuration for encoding anchor information.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Anchor encoder type	SparseBox3DEncoder
vel_dims	int	Velocity dimensions	3	1	infinity
embed_dims	list	Embedding dimensions	[128, 32, 32, 64]				false
mode	categorical	Mode	cat			cat,add
output_fc	bool	Fully Connected Layer	False
in_loops	int	In loops	1	1	infinity
out_loops	int	Out loops	4	1	infinity
pos_embed_only	bool	Pos embed only	False

Sampler Configuration (model.head.sampler)

Configuration for sampling positive and negative examples during training.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
num_dn_groups	int	Number of De-Noising groups	5	1	infinity
num_temp_dn_groups	int	Number of temporal DN groups	3	0	infinity
dn_noise_scale	list	De-Noising scale	[2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]				false
max_dn_gt	int	Maximum DN ground truth	128	1	infinity
add_neg_dn	bool	Add negative DN	True
cls_weight	float	Classification weight	2.0	0	infinity
box_weight	float	Box weight	0.25	0	infinity
reg_weights	list	Regression weights	[2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0]				false
use_temporal_align	bool	Use temporal alignment	False
gt_assign_threshold	float	Ground Truth assign threshold	0.5	0	1

Loss Configuration (model.head.loss)

This section details the different loss components used in the model head.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
cls	collection	Classification loss config					false
reg	collection	Regression loss config					false
id	collection	ID loss config					false

Classification Loss (model.head.loss.cls)

Configuration for the classification loss.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Classification loss type	focal
use_sigmoid	bool	Use sigmoid	True
gamma	float	Focal loss gamma	2.0	0	infinity
alpha	float	Focal loss alpha	0.25	0	1
loss_weight	float	Loss weight	2.0	0	infinity

Regression Loss (model.head.loss.reg)

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Regression loss type	sparse_box_3d
box_weight	float	Box loss weight	0.25	0	infinity
cls_allow_reverse	list	Class allow reverse	[]				false

ID Loss (model.head.loss.id)

Configuration for the ID / Re-ID loss.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	ID loss type	cross_entropy_label_smooth
num_ids	int	Number of IDs	70	1	infinity

BNNeck Configuration (model.head.bnneck)

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Batch Normalization Neck	bnneck
feat_dim	int	Feature dimension	256	1	infinity
num_ids	int	Number of IDs	70	1	infinity

KPS Generator Configuration (model.head.deformable_model.kps_generator)

Configuration for KeyPoint (Sampling) Generator.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
embed_dims	int	Embedding dimensions	256	1	infinity
num_learnable_pts	int	Number of learnable points	6	1	infinity
fix_scale	list	Fixed scale	[[0, 0, 0], [0.45, 0, 0], [-0.45, 0, 0], [0, 0.45, 0], [0, -0.45, 0], [0, 0, 0.45], [0, 0, -0.45]]				false

Refine Layer Configuration (model.head.refine_layer)

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Refine layer type	sparse_box_3d_refinement_module
embed_dims	int	Embedding dimensions	256	1	infinity
refine_yaw	bool	Refine yaw	True
with_quality_estimation	bool	With quality estimation	True

Graph Model Configuration (model.head.graph_model and model.head.temp_graph_model)

Configuration for graph-based modeling (e.g., GNN or attention) used for spatial and temporal relations.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Graph model type	MultiheadAttention
embed_dims	int	Embedding dimensions	512	1	infinity
num_heads	int	Number of heads	8	1	infinity
batch_first	bool	Batch first	True
dropout	float	Dropout rate	0.1	0	1

Decoder Configuration (model.head.decoder)

Configuration for the final output decoder.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Decoder type	SparseBox3DDecoder
score_threshold	float	Score threshold	0.05	0	1

Norm Layer Configuration (model.head.norm_layer and model.head.ffn.pre_norm)

Configuration for normalization layers.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Norm layer type	LN
normalized_shape	int	Normalized shape	256	1	infinity

FFN Configuration (model.head.ffn)

Configuration for Feed-Forward Networks used in the decoder layers.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	FFN type	AsymmetricFFN
in_channels	int	In channels	512	1	infinity
pre_norm	collection	Pre-norm config					false
embed_dims	int	Embedding dimensions	256	1	infinity
feedforward_channels	int	Feedforward channels	1024	1	infinity
num_fcs	int	Number of feedforward channels	2	1	infinity
ffn_drop	float	FFN dropout	0.1	0	1
act_cfg	collection	Activation config					false

Activation Configuration (model.head.ffn.act_cfg)

Configuration for activation functions.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Activation type	ReLU
inplace	bool	Inplace	True

Visibility Net Configuration (model.head.visibility_net)

Configuration for the visibility prediction network.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	VisibilityNet type	visibility_net
embedding_dim	int	Embedding dimension	256	1	infinity
hidden_channels	int	Hidden channels	32	1	infinity

Neck Configuration (model.neck)

Configuration for the model’s neck (Feature Pyramid Network).

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	categorical	Neck - Feature Pyramid Network	FPN			FPN
num_outs	int		4	1	infinity
start_level	int	Start level for FPN	0	0	infinity
out_channels	int	Output channels	256	1	infinity
in_channels	list	Input channels	[256, 512, 1024, 2048]				false
add_extra_convs	categorical	Type of extra conv	on_output			on_input,on_lateral,on_output,False
relu_before_extra_convs	bool	Apply ReLU before extra convs	True

Depth Branch Configuration (model.depth_branch)

Configuration for the depth estimation branch.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	string	Depth branch type	dense_depth
embed_dims	int	Embedding dimensions	256	1	infinity
num_depth_layers	int	Number of depth layers	3	1	infinity
loss_weight	float	Weight for depth loss	0.2	0	infinity

train

The train config contains the parameters related to training. They are described as follows:

train:
  num_epochs: 5
  num_nodes: 1
  num_gpus: 1
  validation_interval: 1
  checkpoint_interval: 1
  pretrained_model_path: ???
  precision: bf16
  optim:
    type: "adamw"
    lr: 0.0001
    weight_decay: 0.001
    paramwise_cfg:
      custom_keys:
        img_backbone:
          lr_mult: 0.25
    grad_clip:
      max_norm: 25
      norm_type: 2
    lr_scheduler:
      policy: "cosine"
      warmup: "linear"
      warmup_iters: 500
      warmup_ratio: 0.333333
      min_lr_ratio: 0.001

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
num_gpus	int	The number of GPUs to run the train job	1	1
gpu_ids	list	List of GPU IDs to run the training on. The length of this list must be equal to the number of gpus in train.num_gpus	[0]				false
num_nodes	int	Number of nodes to run the training on. If > 1, then multi-node is enabled	1	1
seed	int	The seed for the initializer in PyTorch. If < 0, disable fixed seed	1234	-1	infinity
cudnn	collection						false
num_epochs	int	Number of epochs to run the training	10	1	infinity
checkpoint_interval	float	Checkpoint interval in epochs	0.5	0	infinity
validation_interval	float	Validation interval in epochs	0.5	0	infinity
resume_training_checkpoint_path	string	Path to the checkpoint to resume training from
results_dir	string	Path to where all the assets generated from a task are stored
pretrained_model_path	string	Path to pretrained model
optim	collection	Optimizer configuration					false
precision	categorical	Precision	bf16			bf16,fp16,fp32

optim

The optim parameter defines the config for the AdamW optimizer in training, including the learning rate, learning scheduler, and weight decay.

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
type	categorical	Optimizer type	adamw			adamw,adam,sgd
lr	float	Learning rate	5e-05	0	infinity		TRUE
weight_decay	float	Weight decay coefficient	0.001
momentum	float	Momentum for SGD	0.9
paramwise_cfg	collection	Parameter-wise configuration	{‘custom_keys’: {‘img_backbone’: {‘lr_mult’: 0.2}}}				false
grad_clip	collection	Gradient clipping configuration	{‘max_norm’: 25, ‘norm_type’: ‘L2’}				false
lr_scheduler	collection	Learning rate scheduler configuration	{‘policy’: ‘cosine’, ‘warmup’: ‘linear’, ‘warmup_iters’: 500, ‘warmup_ratio’: 0.333333, ‘min_lr_ratio’: 0.001}				false

evaluate

The evaluate config contains the parameters related to evaluation. Currently, we only support evaluation on a single GPU with batch size 1. The parameters are described as follows:

evaluate:
  checkpoint: ${results_dir}/train/sparse4d_model_latest.pth

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
checkpoint	string	Path to the checkpoint used for evaluation	???
results_dir	string	Path to where all the assets generated from a task are stored
metrics	list	Metrics to evaluate	[‘detection’]				false
tracking	collection	Tracking config					false

Set the evaluate checkpoint path in the evaluate specification:

visualize

The visualize config contains the parameters related to visualization. They are described as follows:

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
show	bool	Show visualization	True
vis_dir	string	Visualization directory	./vis
vis_score_threshold	float	Visualization score threshold	0.25	0	1
n_images_col	int	Number of images per column	6	1	infinity
viz_down_sample	int	Visualization down sample	3	1	infinity

inference

The inference config contains the parameters related to training. Currently, we only support inference on a single GPU with batch size 1. They are described as follows:

inference:
  checkpoint: ???
  output_nvschema: true
  jsonfile_prefix: "sparse4d_pred"

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
checkpoint	string	Path to checkpoint file	???
results_dir	string	Path to where all the assets generated from a task are stored
jsonfile_prefix	string	JSON file prefix	sparse4d_pred
output_nvschema	bool	Output NVSchema	True
tracking	collection	Tracking config					false

Set the inference checkpoint path in the inference specification:

export

The export config contains the parameters related to export. Currently, we only support export with batch size 1 and dynamic number of camera sensors. They are described as follows:

export:
  results_dir: ???
  checkpoint: ???
  onnx_file: ???

Field	value_type	description	default_value	valid_min	valid_max	valid_options	automl_enabled
results_dir	string	Path to where all the assets generated from a task are stored
checkpoint	string	Path to the checkpoint file to run export	???
onnx_file	string	Path to the onnx model file	???

Set the export checkpoint path in the export specification:

Training the Model

Use the following command to run Sparse4D training:

Evaluating the Model

The evaluation metrics for Sparse4D are the mean average precision and ranked accuracy.

Use the following command to run Sparse4D evaluation:

Running Inference on the Model

Use the following command to run inference on Sparse4D with the .pth model.

The output will be a file with JSON logs consisting of object detection and tracking results for each frame.

The expected output is as follows:

{
    "version": "4.0",
    "id": "1", # Frame ID
    "sensorId": "bev-sensor-zone-c4", # BEV Sensor ID
    "timestamp": "2025-01-15T10:30:00.123Z", # Timestamp
    "objects": [
      {
        "id": "1", # Object ID
        "type": "Person", # Object Type
        "confidence": 0.887, # Object Confidence Score
        "coordinate": {
          "x": -1.5, # Object Center X Coordinate
          "y": 3.2, # Object Center Y Coordinate
          "z": 0.75 # Object Center Z Coordinate
        },
        "bbox3d": {
          "coordinates": [
            -1.5, # Object Centeroid X Coordinate
            3.2, # Object Centeroid Y Coordinate
            0.75, # Object Centeroid Z Coordinate
            0.5, # Object Width
            0.5, # Object Length
            0.5, # Object Height
            0.0, # Object Pitch
            0.0, # Object Roll
            1.57 # Object Yaw
          ],
          "embedding": [
            {} # Object Embedding
          ],
          "confidence": 0.887 # Object Confidence Score
        }
      },
      {
        "id": "2",
        "type": "Humanoid",
        "confidence": 0.752,
        "coordinate": {
          "x": 5.1,
          "y": -2.8,
          "z": 0.15
        },
        "bbox3d": {
          "coordinates": [
            5.1,
            -2.8,
            0.15,
            1.2,
            1.0,
            0.2,
            0.0,
            0.0,
            -1.04
          ],
          "embedding": [
            {}
          ],
          "confidence": 0.752
        }
      }
    ]
  }
  {
    # ... more frames
  }

Exporting the Model

Use the following command to export Sparse4D to .onnx format for deployment:

Quantization

Sparse4D supports PTQ via TAO Quant using either the torchao (weight-only) or modelopt (static PTQ) backends.

• Add a quantize section to your experiment specification (see TAO Quant documentation for schema and backend options).

• Run:

• Use the quantized checkpoint by setting evaluate.is_quantized: true or inference.is_quantized: true and pointing to the artifact saved under results_dir (for example, quantized_model_torchao.pth or quantized_model_modelopt.pth). For ModelOpt artifacts, the model weights are stored under model_state_dict.

Notes

• For modelopt static PTQ, ensure that your dataset configuration provides a representative calibration loader.

• For torchao, activation settings in the configuration are ignored.

Calibration Dataset (ModelOpt)

When you use the modelopt backend (static PTQ), provide a calibration dataset via dataset.quant_calibration_dataset.

Minimal example:

quantize:
  backend: "modelopt"
  mode: "static_ptq"
  algorithm: "minmax"
dataset:
  quant_calibration_dataset:
    images_dir: "/path/to/calib/images"

See also: TAO Quant overview and its Configuration and backend pages.

TensorRT engine generation and deploying to DeepStream

Refer to the Nvidia Spatial AI documentation page for more information about deploying a Sparse4D model to DeepStream via TensorRT engine generation.