model:\n  type: \"sparse4d\"\n  use_grid_mask: true\n  use_deformable_func: true\n  use_temporal_align: true\n  input_shape: [960, 540]\n  embed_dims: 256\n  neck:\n    type: \"FPN\"\n    num_outs: 4\n    start_level: 0\n    out_channels: 256\n    in_channels: [256, 512, 1024, 2048]\n    add_extra_convs: \"on_output\"\n    relu_before_extra_convs: true\n  depth_branch:\n    type: \"dense_depth\"\n    embed_dims: \"${model.embed_dims}\"\n    num_depth_layers: 3\n    loss_weight: 0.2\n  head:\n    type: \"sparse4d\"\n    num_output: 300\n    cls_threshold_to_reg: 0.05\n    decouple_attn: true\n    return_feature: true\n    use_reid_sampling: false\n    embed_dims: \"${model.embed_dims}\"\n    num_groups: 8\n    num_decoder: 6\n    num_single_frame_decoder: 1\n    drop_out: 0.1\n    temporal: true\n    with_quality_estimation: true\n    instance_bank:\n      num_anchor: 900\n      anchor: ???\n      num_temp_instances: 600\n      confidence_decay: 0.8\n      feat_grad: false\n      default_time_interval: 0.033333\n      embed_dims: \"${model.embed_dims}\"\n      use_temporal_align: \"${model.use_temporal_align}\"\n    anchor_encoder:\n      type: 'SparseBox3DEncoder'\n      vel_dims: 3\n      embed_dims: [128, 32, 32, 64]\n      mode: 'cat'\n      output_fc: false\n      in_loops: 1\n      out_loops: 4\n    operation_order: [\n      \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n      \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n      \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n      \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n      \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n      \"deformable\", \"ffn\", \"norm\", \"refine\"\n    ]\n    temp_graph_model:\n      type: \"MultiheadAttention\"\n      embed_dims: 512\n      num_heads: 8\n      batch_first: true\n      dropout: 0.1\n    graph_model:\n      type: \"MultiheadAttention\"\n      embed_dims: \"${model.head.temp_graph_model.embed_dims}\"\n      num_heads: \"${model.head.temp_graph_model.num_heads}\"\n      batch_first: true\n      dropout: \"${model.head.temp_graph_model.dropout}\"\n    norm_layer:\n      type: \"LN\"\n      normalized_shape: \"${model.embed_dims}\"\n    ffn:\n      type: \"AsymmetricFFN\"\n      in_channels: 512\n      pre_norm:\n        type: \"LN\"\n      embed_dims: 256\n      feedforward_channels: 1024\n      num_fcs: 2\n      ffn_drop: 0.1\n      act_cfg:\n        type: \"ReLU\"\n        inplace: true\n    deformable_model:\n      embed_dims: \"${model.embed_dims}\"\n      num_groups: 8\n      num_levels: 4\n      attn_drop: 0.15\n      use_deformable_func: true\n      use_camera_embed: true\n      residual_mode: \"cat\"\n      kps_generator:\n        embed_dims: \"${model.embed_dims}\"\n        num_learnable_pts: 6\n        fix_scale:\n          - [0, 0, 0]\n          - [0.45, 0, 0]\n          - [-0.45, 0, 0]\n          - [0, 0.45, 0]\n          - [0, -0.45, 0]\n          - [0, 0, 0.45]\n          - [0, 0, -0.45]\n    refine_layer:\n      type: \"SparseBox3DRefinementModule\"\n      embed_dims: \"${model.embed_dims}\"\n      refine_yaw: true\n      with_quality_estimation: true\n    sampler:\n      num_dn_groups: 5\n      num_temp_dn_groups: 3\n      dn_noise_scale: [2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n      max_dn_gt: 128\n      add_neg_dn: true\n      cls_weight: 2.0\n      box_weight: 0.25\n      reg_weights: [2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0]\n      use_temporal_align: \"${model.use_temporal_align}\"\n    visibility_net:\n      type: \"visibility_net\"\n      embedding_dim: 256\n      hidden_channels: 32\n    loss:\n      reg:\n        type: \"sparse_box_3d\"\n        box_weight: 0.25\n        cls_allow_reverse: [5, 6, 7]\n      cls:\n        type: \"focal\"\n        use_sigmoid: true\n        gamma: 2.0\n        alpha: 0.25\n        loss_weight: 2.0\n      id:\n        type: \"cross_entropy_label_smooth\"\n        num_ids: \"${dataset.num_ids}\"\n    bnneck:\n      type: \"bnneck\"\n      feat_dim: 256\n      num_ids: \"${dataset.num_ids}\"\n    decoder:\n      type: \"SparseBox3DDecoder\"\n      score_threshold: 0.05\n    reg_weights: [2.0, 2.0, 2.0, 1 ,1, 1, 1, 1, 1, 1, 1]

","code--vjvtzf","Person","code--uxnr56","Fourier_GR1_T2_Humanoid","code--1rmzh3","Agility_Digit_Humanoid","code-6at35s","Nova_Carter","code--r0p6y","Transporter","code-8vtxpp","Forklift","code-1u","B","code-1v","C","code-26","N","code-20","H","code-2f","W","code-29","Q","code-25","M","code-1x","E","code-28jn","img","code-irpshc","projection_mat","code--e7s897","image_wh","code--sfwlxu","input_cached_feature","code-60o7nh","input_cached_anchor","code--q1ko48","prev_exists","code--nc0xju","interval_mask","code--xoy97t","boxes_3d","code--1fnwu5","(x, y, z, width, length, height, yaw, velocity_x, velocity_y, velocity_z)","code-z82nhr","scores_3d","code--a1acz3","labels_3d","code-7f8yqe","cls_scores","code--c6ztv6","instance_ids","code-1bhro7","instance_feats","code-vr37",".pkl","code--7fx5g1","metadata","code-1nqt8l","infos","code-5tc6hk","version","code-2g4x","str","code-dvhfl8","'trainval_split'","code--5mb3y9","split_type","code-n87kd","'all'","code-90qxnv","frame_idx","code-28kv","int","code-1c","0","code-1tabo","cams","code-1u306","dict","code-o7pojf","'Camera'","code--mtmhcd","'Camera_01'","code-yemgda","scene_name","code--22cw8l","'Warehouse_014+bev-sensor-training-1'","code-wtjpy","timestamp","code-1m2bto","float","code-10qa","0.0","code-1tta7t","token","code-s8ph9v","'Warehouse_014+bev-sensor-training-1__000000000'","code-l7gaiz","group_name","code-6z4fjr","'bev-sensor-training-1'","code--mva8te","instance_inds","code--5szpvt","np.ndarray","code--mdp7gy","array([372, 171, 172, 631, 663])","code-z7t36r","asset_inds","code--x5i56y","array([ 771, 772, 775, 2094, 1372])","code-tv3hev","gt_boxes","code--z3fn5k","[x, y, z, dx, dy, dz, heading]","code-eqmaqx","array([[-0.686, 1.073, ..., -0. ], ...])","code-u1fuk6","gt_names","code--x6zdi1","array(['agility_digit', 'gr1_t2', ...], dtype='<U18')","code-8ao5in","gt_velocity","code--wgu5pr","[vx, vy, vz]","code--7tef3a","array([[0., 0., 0.], ...])","code--ra6gh","valid_flag","code--h3j6sy","array([ True, True, ...])","code-b1hoys","gt_visibility","code-1z7a6","list","code-8xrruu","[{'Camera': 1.0, 'Camera_01': 1.0, ...}, ...]","code--60f3om","data_path","code-1tx808","tuple","code-nd2t6h","(h5_file_path, rgb_image_relative_path)","code-9qjsgj","('data/mtmc/Warehouse_014/Camera.h5', 'rgb/rgb_00000.jpg')","code-x6ejac","depth_map_path","code-9r13sz","(h5_file_path, depth_image_relative_path)","code--kyz1y1","('data/mtmc/Warehouse_014/Camera.h5', 'distance_to_image_plane_png/distance_to_image_plane_00000.png')","code-4yawux","sample_data_token","code--ixp4wl","'Warehouse_014+bev-sensor-training-1__000000000+Camera'","code-snk6fp","cam_intrinsic","code--4q3205","array([[916.249, 0., 960.], [0., 916.249, 540.], [0., 0., 1.]])","code--hukhix","sensor2world_transform","code--a12p8k","array([[0.018, -0.999, ..., -4.731], ..., [0., 0., 0., 1.]])","code--j1k93u","sparse4d_rn50","code--m3jhfd","sparse4d_rn101","code-ayfwg","pallet_truck","code--gefxd7","person","code--khv43t","gr1_t2","code-9vyf8z","agility_digit","code--64r7wg","nova_carter","code--sc0wve","transporter","code-7tcdzh","forklift","code-cuthyx","_ov_kmeans900_v2.2_r50.npy","code--fb8goq","_ov_kmeans900_v2.2_r101.npy","code-1pykvd","model","nvidia/tao/sparse4d_rn50","key","nvidia/tao/models/sparse4d_rn50","resourceType","models","resourceId","sparse4d_rn50","shortDescription","TAO Pretrained Sparse4D with ResNet-50 Backbone","# TAO Sparse4D Model Card\n\n## Model Overview \n\n### Description:\nTAO Sparse4D is an advanced three-dimensional (3D) multi-camera detection and tracking network. It generates precise 3D bounding boxes and tracking IDs for a diverse set of objects across multiple camera views. The model was developed by NVIDIA as a part of the TAO model family. The provided model is pre-trained on the Multi-Target Multi-Camera (MTMC) Tracking 2025 subset from the [NVIDIA PhysicalAI-SmartSpaces dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-SmartSpaces/tree/main) also utilized for the 2025 AI City Challenge. This model is ready for commercial use.\n\n\n

\n\nThe model in this card was trained & evaluated on the following warehouse-centric classes: `Person`, `Fourier_GR1_T2_Humanoid`, `Agility_Digit_Humanoid`, `Nova_Carter`, `Transporter` and `Forklift`.\n\n### License/Terms of Use: \n\nGOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n### Deployment Geography:\nGlobal\n
\n\n### Use Case:
\nTAO Sparse4D is designed for 3D multi-camera object detection and tracking in indoor environments like warehouses and logistics facilities. The model detects & tracks objects across multiple camera views for applications including warehouse automation, safety and workflow optimization in industrial settings, providing spatial understanding of a scene.\n\n\n### Release Date:
\nv1.0 Model - NGC - 06/13/2025
\nv1.1 Model - NGC - 11/30/2025
\nv2.0 Model - NGC - 01/23/2026 via [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/sparse4d_rn101/files?version=deployable_v2.0)
\nv2.1 Model - NGC - 02/27/2026 via [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/sparse4d_rn101/files?version=deployable_v2.1)
\nv2.2 Model (ResNet50) - NGC - 05/12/2026 via [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/sparse4d_rn50/files?version=deployable_v2.2)
\nv2.2 Model (ResNet101) - NGC - 05/12/2026 via [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/sparse4d_rn101/files?version=deployable_v2.2)
\n\n\n## References \n\n- [Wang, Y., Pusegaonkar, S., Wang, Y., Li, A., Kumar, V., Sethi, C., ... & Biswas, S. (2026). A Unified 3D Object Perception Framework for Real-Time Outside-In Multi-Camera Systems. arXiv preprint arXiv:2601.10819.](https://arxiv.org/abs/2601.10819)\n- [Lin, X., Pei, Z., Lin, T., Huang, L., & Su, Z. (2023). Sparse4d v3: Advancing end-to-end 3d detection and tracking. arXiv preprint arXiv:2311.11722.](https://arxiv.org/abs/2311.11722)\n\n\n## Model Architecture \n\n**Architecture Type:** Convolution Neural Network (CNN) based backbone with Transformer based decoder layers.
\n\n**Network Architecture:** ResNet101 backbone with Transformer based decoder layers.
\n\n**Number of model parameters:** 7.8 × 10⁷ parameters\n\nThis work leverages the [Sparse4D v3 model](https://arxiv.org/abs/2311.11722) & is tailored for indoor environments like warehouses with static camera setups. Sparse4D is a query based technique which involves sampling sparse features for better computational efficiency compared to other dense 3D detection & tracking based techniques. The architecture features a ResNet101 backbone and processes time-synchronized frames from multiple cameras. Key components include the backbone, a Feature Pyramid Network (FPN), and multiple decoder layers incorporating Multi-Scale Deformable Aggregation blocks (featuring key-point generation, feature sampling, visibility net module & embedding generation) along with refinement & classification layers. The model is trained on regression, classification & ID losses. The model also consists of an Instance Bank module utilized for tracking ID assignment & management. Along with the 3D bounding box information & object ID, Sparse4D also outputs an instance feature containing high-dimensional semantics from the image encoder. The training algorithm optimizes the network to minimize the classification loss, regression loss & ID loss.\n\n

\n\n\n## Input:
\n\nSparse4D is trained on RGB images, camera calibration files, ground truth 3D bounding boxes & object ids, and optional depth maps. Since performing 3D multi-camera detection and tracking across large regions like warehouses with high-density camera setups is computationally expensive, we partition large regions into overlapping groups called Bird's Eye View (BEV) group. Each BEV group contains multiple cameras and serves as the fundamental training unit for Sparse4D. \n\n**Input Type(s):** Each BEV group will have cameras with RGB images, Camera calibration file, Ground Truth with 3D bounding boxes & Optional Depth Maps available via HuggingFace or generated synthetically by Nvidia Isaac Sim Replicator.
\n\n**Input Format:** Red, Green, Blue (RGB) images stored in raw png/jpg/hdf5 and Depth Maps stored in png/hdf5. Can support input resolution of 3 x 1080 x 1920 for both RGB & Depth Maps. Data preprocessing is required for grouping images & depth maps from multiple cameras to appropriate BEV groups. This can be done via the TAO Data Service.
\n\n**Input Parameters:** Multiple dimensions. See below for detailed model input shapes.
\n\n**Other Properties Related to Input:** 3 x 1080 x 1920 (C x H x W) resolution images for both RGB & Depth Maps. Data Pre-Processing needed via TAO Data Services. No alpha channel required.
\n\nNote that depth maps are optional and not required for evaluation or inference. The raw model inputs are as follows:\n\n| Dimension | Description |\n|-----------|-------------|\n| `B` | Batch size |\n| `C` | Number of channels |\n| `N` | Number of cameras |\n| `H` | Image height |\n| `W` | Image width |\n| `Q` | Number of queries |\n| `M` | Number of output boxes |\n| `E` | Number of instance features |\n\n| Input Name | Type | Shape | ResNet101 Shape | Description |\n|------------|------|-------|---------------|-------------|\n| `img` | List[Tensor] | (B, N, C, H, W) | (1, N, 3, 540, 960) | Input image tensor |\n| `projection_mat` | List[Tensor] | (B, N, 4, 4) | (1, 10, 4, 4) | List of projection matrices |\n| `image_wh` | List[Tensor] | (B, N, 2) | (1, 10, 2) | List of image width and height |\n| `input_cached_feature` | List[Tensor] | (B, 600, 256) | (1, 600, 256) | List of cached features |\n| `input_cached_anchor` | List[Tensor] | (B, 600, 11) | (1, 600, 11) | List of cached anchor |\n| `prev_exists` | List[Tensor] | (B) | (1) | Indicates if previous frame exists or not |\n| `interval_mask` | List[Tensor] | (Bx1x1) | (1x1x1) | Boolean to describe the interval mask |\n\n\n* No. of cameras is dynamic in the model.\n* Model is initialized with zero tensors for the `input_cached_feature`, `input_cached_anchor`, `prev_exists` & `interval_mask` at the first frame. These tensors will be updated automatically via instance bank from the second frame & onwards.\n\n

\n\n\n## Output:
\n**Output Type(s):** Tensors consisting of bounding boxes in 3D , object confidence scores, classes, class confidence scores, tracking object ID & instance features.
\n\n**Output Format:** List of Tensors.
\n\n**Output Parameters:** Multiple dimensions. See below for detailed model output shapes.
\n\n**Other Properties Related to Output:** Please see the details below.
\n\nThe final output is a list with length of batch size after post-processing. Each element is a dictionary with the following keys:\n\n| Output Name | Type | Shape | ResNet Shape | Description |\n|------------|------|-------|---------------|-------------|\n| `boxes_3d` | List[Tensor] | (B, M, 10) | (1, 600, 10) | List of 3D boxes: `(x, y, z, width, length, height, yaw, velocity_x, velocity_y, velocity_z)` in OV coordinates |\n| `scores_3d` | List[Tensor] | (B, M) | (1, 600) | List of object confidence scores (classification scores * centerness score) |\n| `labels_3d` | List[Tensor] | (B, M) | (1, 600) | List of class labels |\n| `cls_scores` | List[Tensor] | (B, M) | (1, 600) | List of classification scores |\n| `instance_ids` | List[Tensor] | (B, M) | (1, 600) | List of instance IDs |\n| `instance_feats` | List[Tensor] | (B, M, E) | (1, 600, 256) | List of instance features |\n\nwhere:\n\n- **x, y, z** - 3D coordinates of the bounding-box centroid in the world coordinate system which is in meters.\n- **width, length, height** - Box dimensions in meters along its x (width), y (length) and z (height) axes of the object-centered coordinate system, with the origin at the centroid.\n- **yaw** - Euler angle in radians about the y-axis of the object-centered coordinate system defining the box’s heading in the world coordinate system. (Pitch and roll are assumed zero.)\n- **velocity** - 3D velocity vector components in meters per second (m/s) in the world coordinate system, representing the object's instantaneous motion along the x, y, and z axes respectively.\n\n

\n\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. \n\n## Software Integration:\n\n**Runtime Engine(s):** \n* DeepStream - 7.1
\n* TAO - 6.1.0
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Blackwell
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n* NVIDIA Ampere
\n\n**Preferred/Supported Operating Systems:**
\n* Linux
\n\nThese models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA DataCenter GPU. For software, the models are specifically designed for the [TAO Toolkit](https://github.com/NVIDIA/tao_pytorch_backend).\n\n## Model versions (Latest):\n\n- **trainable_v2.2** (ResNet50 / ResNet101) - Pre-trained model for Sparse4D.\n- **deployable_v2.2** (ResNet50 / ResNet101) - Model for Sparse4D deployable to DeepStream or TensorRT.\n\n\n## Training, Testing, and Evaluation Datasets \n\n### Dataset Overview\n** Total Size:\n\n|Subset|No. of scenes|No. BEV groups|No. of objects per scene|No. of cameras per scene|Duration of each camera sequence|FPS|\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|Train|90|3469|1-120|1-20|5 mins|30 FPS|\n\n** Total Number of Datasets: 3 Datasets in total including (MTMC Tracking 2025 and COSMOS augmented version of the MTMC Tracking 2025 dataset, SCOUT dataset)\n\n** Dataset partition: Training 80%, Testing 10%, Validation 10%
\n** Time period for training data collection: Collected throughout the year 2024-2025.
\n** Time period for testing data collection Collected throughout the year 2024-2025.
\n** Time period for validation data collection Collected throughout the year 2024-2025.
\n\nThe model is trained on the MTMC Tracking 2025 dataset available on [HuggingFace](https://huggingface.co/datasets/nvidia/PhysicalAI-SmartSpaces/tree/main/MTMC_Tracking_2025). \n\n### Data Format & Preprocessing\n\n#### Raw Data (MTMC Tracking 2025/AI City Challenge Format)\n\nThis raw data includes .mp4 RGB video files, depth maps stored in HDF5 format, ground_truth.json, calibration.json & map.png. For more details on these files including the ground truth format please refer to the raw dataset format [here](https://huggingface.co/datasets/nvidia/PhysicalAI-SmartSpaces#directory-structure-for-mtmc_tracking_2025). \n\nThe raw data may also be obtained from Nvidia Isaac Sim Replicator Agent [IRA](https://docs.isaacsim.omniverse.nvidia.com/latest/replicator_tutorials/tutorial_replicator_agent.html). When collected via this route, RGB image data & depth maps can be in frame output/HDF5 output. Utilize the TAO Data Service to convert your raw data format (AICity) to pickle format (OVPKL) accordingly.\n\n#### Pickle File Structure (OVPKL format)\n\nThe above raw data format needs to be converted to a pickle files for model training via the TAO DataService. The model will utilize these pickle files along with the above files for training. An example command can be found in the sparse4d finetuning notebook. The pickle file will consist of the items.\n\n\nThe process generates pickle files (`.pkl`) containing scene information. Each pickle file is a dictionary with the following top-level keys:\n\n- `metadata`: (dict) Contains metadata about the dataset.\n- `infos`: (list) A list of dictionaries, where each dictionary contains information about a specific frame in the scene.\n\n##### `metadata` key\n\nThe `metadata` dictionary contains the following keys:\n\n| Key | Type | Description | Example Value |\n| :--------- | :----- | :------------------------------------------- | :------------------------------------ |\n| `version` | `str` | Version of the dataset or split. | `'trainval_split'` |\n| `split_type` | `str` | Type of data split (e.g., 'all', 'train', 'validation'). | `'all'` |\n\n##### `infos` key\n\nThe `infos` key holds a list of dictionaries. Each dictionary in this list corresponds to a frame and contains the following keys:\n\n| Key | Type | Description | Example Value (Illustrative) |\n| :---------------- | :---------- | :--------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------- |\n| `frame_idx` | `int` | Index of the frame. | `0` |\n| `cams` | `dict` | Dictionary containing data for each camera in the frame. Keys are camera names (e.g., `'Camera'`, `'Camera_01'`). | See [cams Structure](#cams-structure) below. |\n| `scene_name` | `str` | Name of the scene. | `'Warehouse_014+bev-sensor-training-1'` |\n| `timestamp` | `float` | Timestamp of the frame. | `0.0` |\n| `token` | `str` | Unique token for the frame. | `'Warehouse_014+bev-sensor-training-1__000000000'` |\n| `group_name` | `str` | Name of the group this frame belongs to. | `'bev-sensor-training-1'` |\n| `instance_inds` | `np.ndarray`| Array of instance indices present in the frame. | `array([372, 171, 172, 631, 663])` |\n| `asset_inds` | `np.ndarray`| Array of asset indices corresponding to instances. | `array([ 771, 772, 775, 2094, 1372])` |\n| `gt_boxes` | `np.ndarray`| Ground truth bounding boxes for objects in the frame. Shape: (N, 7) where N is the number of objects. Each row is `[x, y, z, dx, dy, dz, heading]`. | `array([[-0.686, 1.073, ..., -0. ], ...])` |\n| `gt_names` | `np.ndarray`| Array of ground truth names for objects. | `array(['agility_digit', 'gr1_t2', ...], dtype='\n\n\n### Training Data \n\nThe dataset statistics used for pretraining the model are as follows:\n\n* Data Modality - Image
\n\n* Image Training Data Size - 1 Million to 1 Billion Images
\n\n* Data Collection Method by dataset - Synthetic
\n\n* Labeling Method by dataset - Synthetic
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\n\nPlease see above.\n\n* Model was trained on the following moving object classes: `Person`, `Fourier_GR1_T2_Humanoid`, `Agility_Digit_Humanoid`, `Nova_Carter`, `Transporter` and `Forklift`.\n* Both validation & testing was conduction on random scenes from the MTMC Tracking 2025 subset.\n\n\n## Testing and Evaluation Datasets\n\nBoth validation & testing was conduction on random scenes from the MTMC Tracking 2025 subset. \n\n* Data Collection Method by dataset - Synthetic
\n\n* Labeling Method by dataset - Synthetic
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\nWe utilize a random scenes from the test set of the MTMC Tracking 2025 subset.\n\n\n### Methodology and KPI\n\nThe key performance indicator is Higher Order Tracking Accuracy (HOTA) per-class and the average HOTA obtained across all classes. We utilize the HOTA evaluation methodology to assess multi-object tracking accuracy.\n\nHigher Order Tracking Accuracy (HOTA) is a metric that balances detection accuracy and association accuracy into a single unified score. It decomposes tracking performance into Detection Accuracy (DetA), which measures how well objects are localized, and Association Accuracy (AssA), which measures how well detections are linked over time into consistent tracks. HOTA is computed as the geometric mean of DetA and AssA, averaged over a range of localization thresholds. This provides a balanced, holistic measure of tracking quality that captures both spatial accuracy and temporal consistency.\n\nThe following scores are for v2.2 models trained on MTMC Tracking 2025 subset. The evaluation set and training set is disjoint.\n\n#### ResNet50 backbone (v2.2)\n\n| Object Class | HOTA | DetA | AssA | LocA |\n|---------------------------|-------|-------|-------|-------|\n| `Person` | 53.93 | 59.13 | 49.54 | 73.34 |\n| `Fourier_GR1_T2_Humanoid` | 59.31 | 59.87 | 58.84 | 73.46 |\n| `Agility_Digit_Humanoid` | 52.69 | 52.57 | 53.50 | 70.72 |\n| `Nova_Carter` | 58.26 | 56.89 | 59.92 | 71.05 |\n| `Transporter` | 33.70 | 38.98 | 31.49 | 68.41 |\n| `Forklift` | 48.05 | 43.84 | 54.97 | 66.69 |\n| Average | 50.99 | 51.88 | 51.37 | 70.61 |\n\n#### ResNet101 backbone (v2.2)\n\n| Object Class | HOTA | DetA | AssA | LocA |\n|---------------------------|-------|-------|-------|-------|\n| `Person` | 57.96 | 58.99 | 57.11 | 73.29 |\n| `Fourier_GR1_T2_Humanoid` | 60.93 | 60.97 | 60.98 | 74.00 |\n| `Agility_Digit_Humanoid` | 54.00 | 53.32 | 55.32 | 71.02 |\n| `Nova_Carter` | 54.85 | 57.60 | 53.28 | 70.92 |\n| `Transporter` | 33.98 | 38.13 | 32.58 | 67.33 |\n| `Forklift` | 48.05 | 43.78 | 55.20 | 66.45 |\n| Average | 51.63 | 52.13 | 52.41 | 70.50 |\n\n\n## Real-time Inference Performance \n\nInference runs through the DeepStream pipeline on TensorRT with mixed precision (FP16+FP32). The TensorRT columns capture model-only latency, while the DeepStream columns add the instance-bank pre- and post-processing overhead. The table summarizes how many cameras each GPU supports at 30, 15, and 10 FPS for the v2.0 model. Numbers in **bold** are measured (all TensorRT values and certain DeepStream values). DeepStream values not in bold are estimates based on the guidance that the DS microservice adds approximately 30% overhead over Sparse4D TensorRT model performance.\n\n| GPU | TensorRT @30 FPS | DeepStream @30 FPS | TensorRT @15 FPS | DeepStream @15 FPS | TensorRT @10 FPS | DeepStream @10 FPS |\n| :-- | ---------------: | -----------------: | ---------------: | -----------------: | ---------------: | -----------------: |\n| 1 x A100-SXM4-80GB | **13** | 9 | **28** | 19 | **43** | 30 |\n| 1 x B200 | **75** | 52 | **155** | 108 | **200** | 140 |\n| 1 x GB200 | **101** | 70 | **200** | 140 | **200** | 140 |\n| 1 x H100 NVL - 94GB | **23** | 16 | **48** | 33 | **72** | 50 |\n| 1 x H100 SXM HBM3 - 80GB | **28** | 19 | **58** | 40 | **89** | 62 |\n| 1 x H200 | **32** | 22 | **67** | 46 | **103** | 72 |\n| 1 x L4 - 24GB | **3** | 2 | **6** | 4 | **9** | 6 |\n| 1 x L40 | **8** | 5 | **16** | 11 | **26** | 18 |\n| 1 x L40S - 48GB | **9** | 6 | **20** | 14 | **30** | 21 |\n| 1 x RTX 6000 ADA | **9** | 6 | **19** | 13 | **27** | 18 |\n| 1 x RTX PRO 6000 Blackwell (Server) | **28** | 19 | **58** | 40 | **89** | 62 |\n| 1 x RTX PRO 6000 Blackwell (Workstation) | **29** | 20 | **59** | 41 | **90** | 63 |\n| 1 x IGX Thor - T7000 iGPU (no dGPU) | **5** | 3 | **11** | 7 | **17** | 11 |\n| 1 x IGX Thor - T7000 dGPU - RTX PRO 6000 Blackwell Max-Q Workstation Edition | **21** | 14 | **30** | 21 | **30** | 21 |\n| 1 x AGX Thor - T5000 | **4** | 2 | **8** | 5 | **14** | 9 |\n| 1 x DGX Spark | **5** | 3 | **11** | 7 | **17** | 11 |\n\n\n## Changelog\n\n### v2.2\n- Released two backbone variants of the same recipe: ResNet50 (lower latency / smaller footprint) and ResNet101 (high accuracy), shipped under `sparse4d_rn50` and `sparse4d_rn101` NGC model cards respectively.\n- The ResNet50 variant is built on top of an RT-DETR R50 pre-training initialization for stronger image features at half the backbone cost.\n- Added `pallet_truck` to the trained class set (`person`, `gr1_t2`, `agility_digit`, `nova_carter`, `transporter`, `forklift`, `pallet_truck`).\n- Expanded the training corpus to all available MTMC Tracking 2025 scenes (lazy-loaded), 3K+ BEV groups, and refreshed COSMOS-augmented data; mixed FPS training is unchanged.\n- Refreshed kmeans anchor priors per backbone (`_ov_kmeans900_v2.2_r50.npy`, `_ov_kmeans900_v2.2_r101.npy`).\n\n### v2.1\n- Trained on a significantly expanded dataset: more synthetic environments (retail, hospital, supermarket), GTC'24 warehouse data, COSMOS-transferred data with BEV groups, and real-world SCOUT data.\n- Leverages the upgraded camera grouping algorithm (SDU v1.2).\n- Corrected forklift orientation annotations.\n\n### v2.0\n- Increased the number of BEV groups to 1337 to improve accuracy across all benchmarks.\n- Added COSMOS-augmented datasets to the training set, using an 80:20 split between synthetic data and COSMOS augmentations.\n- Introduced multiple FPS settings in training to better handle low-frame-rate streams (1, 2, 3, 5, 6, 10, 15, and 30 FPS).\n- Increased the regression weight for rotation loss to improve bounding box fitting accuracy.\n\n### v1.1\n- Updated input image resolution to 960×540 and introduced camera embeddings to lift maximum camera limits.\n- Expanded training set to 34 scenes spanning 500+ cameras and 380 BEV groups (1–18 cameras each).\n- Supported classes: `Person`, `Fourier_GR1_T2_Humanoid`, `Agility_Digit_Humanoid`, `Nova_Carter`, `Transporter`, `Forklift`.\n- Mixed input frame rates (10, 15, 30 FPS) to improve robustness to lower streaming FPS.\n- Enabled sparser camera grouping with random frame dropping to mirror Data Service frame-loss scenarios.\n- Fixed rotation bug in object annotations and retrained the model with corrected labels.\n- New MSDA FP16 Kernel which helps improve throughput by ~1.5x-2x for Data Center GPUs available in the DeepStream Pipeline inside the Nvidia Video Search and Summarization (VSS) - Warehouse Blueprint release.\n\n### v1.0\n- First commercial warehouse-focused 3D detection and tracking release.\n- Input image resolution: 1408×512 and cameras limited to 20.\n- Supported classes: `Person`, `Fourier_GR1_T2_Humanoid`, `Agility_Digit_Humanoid`, `Nova_Carter`.\n\n\n## How to use this model \n\nIn order to use the model as pre-trained weights for transfer learning, please use the snippet below as a template for the `model` component of the experiment spec file to train a Sparse4D. For more information on experiment spec file, please refer to the [Train Adapt Optimize (TAO) Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\nmodel:\n type: \"sparse4d\"\n use_grid_mask: true\n use_deformable_func: true\n use_temporal_align: true\n input_shape: [960, 540]\n embed_dims: 256\n neck:\n type: \"FPN\"\n num_outs: 4\n start_level: 0\n out_channels: 256\n in_channels: [256, 512, 1024, 2048]\n add_extra_convs: \"on_output\"\n relu_before_extra_convs: true\n depth_branch:\n type: \"dense_depth\"\n embed_dims: \"${model.embed_dims}\"\n num_depth_layers: 3\n loss_weight: 0.2\n head:\n type: \"sparse4d\"\n num_output: 300\n cls_threshold_to_reg: 0.05\n decouple_attn: true\n return_feature: true\n use_reid_sampling: false\n embed_dims: \"${model.embed_dims}\"\n num_groups: 8\n num_decoder: 6\n num_single_frame_decoder: 1\n drop_out: 0.1\n temporal: true\n with_quality_estimation: true\n instance_bank:\n num_anchor: 900\n anchor: ???\n num_temp_instances: 600\n confidence_decay: 0.8\n feat_grad: false\n default_time_interval: 0.033333\n embed_dims: \"${model.embed_dims}\"\n use_temporal_align: \"${model.use_temporal_align}\"\n anchor_encoder:\n type: 'SparseBox3DEncoder'\n vel_dims: 3\n embed_dims: [128, 32, 32, 64]\n mode: 'cat'\n output_fc: false\n in_loops: 1\n out_loops: 4\n operation_order: [\n \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n \"deformable\", \"ffn\", \"norm\", \"refine\", \"temp_gnn\", \"gnn\", \"norm\", \n \"deformable\", \"ffn\", \"norm\", \"refine\"\n ]\n temp_graph_model:\n type: \"MultiheadAttention\"\n embed_dims: 512\n num_heads: 8\n batch_first: true\n dropout: 0.1\n graph_model:\n type: \"MultiheadAttention\"\n embed_dims: \"${model.head.temp_graph_model.embed_dims}\"\n num_heads: \"${model.head.temp_graph_model.num_heads}\"\n batch_first: true\n dropout: \"${model.head.temp_graph_model.dropout}\"\n norm_layer:\n type: \"LN\"\n normalized_shape: \"${model.embed_dims}\"\n ffn:\n type: \"AsymmetricFFN\"\n in_channels: 512\n pre_norm:\n type: \"LN\"\n embed_dims: 256\n feedforward_channels: 1024\n num_fcs: 2\n ffn_drop: 0.1\n act_cfg:\n type: \"ReLU\"\n inplace: true\n deformable_model:\n embed_dims: \"${model.embed_dims}\"\n num_groups: 8\n num_levels: 4\n attn_drop: 0.15\n use_deformable_func: true\n use_camera_embed: true\n residual_mode: \"cat\"\n kps_generator:\n embed_dims: \"${model.embed_dims}\"\n num_learnable_pts: 6\n fix_scale:\n - [0, 0, 0]\n - [0.45, 0, 0]\n - [-0.45, 0, 0]\n - [0, 0.45, 0]\n - [0, -0.45, 0]\n - [0, 0, 0.45]\n - [0, 0, -0.45]\n refine_layer:\n type: \"SparseBox3DRefinementModule\"\n embed_dims: \"${model.embed_dims}\"\n refine_yaw: true\n with_quality_estimation: true\n sampler:\n num_dn_groups: 5\n num_temp_dn_groups: 3\n dn_noise_scale: [2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n max_dn_gt: 128\n add_neg_dn: true\n cls_weight: 2.0\n box_weight: 0.25\n reg_weights: [2.0, 2.0, 2.0, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0]\n use_temporal_align: \"${model.use_temporal_align}\"\n visibility_net:\n type: \"visibility_net\"\n embedding_dim: 256\n hidden_channels: 32\n loss:\n reg:\n type: \"sparse_box_3d\"\n box_weight: 0.25\n cls_allow_reverse: [5, 6, 7]\n cls:\n type: \"focal\"\n use_sigmoid: true\n gamma: 2.0\n alpha: 0.25\n loss_weight: 2.0\n id:\n type: \"cross_entropy_label_smooth\"\n num_ids: \"${dataset.num_ids}\"\n bnneck:\n type: \"bnneck\"\n feat_dim: 256\n num_ids: \"${dataset.num_ids}\"\n decoder:\n type: \"SparseBox3DDecoder\"\n score_threshold: 0.05\n reg_weights: [2.0, 2.0, 2.0, 1 ,1, 1, 1, 1, 1, 1, 1]\n\n```\n\n## Inference & Deployment:\n\nFor deployment of this model, please refer to the our Nvidia Video Search and Summarization (VSS) - Warehouse Blueprint release documentation.\n\n**Acceleration Engine:** Tensor(RT)
\n\n**Test Hardware:**
\n* NVIDIA Datacenter GPUs
\n\n\n### Ethical Considerations \n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. \n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Sub-cards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Sparse4D (ResNet-50)",["D",1780092957862],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/sparse4d_rn50","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",574901189,"licenseTerms",{"_464":465,"_466":467,"_468":469,"_470":30},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[429,430,431,432,433,434,435,436,437],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_395":460,"_439":461,"_440":462,"_442":443},{"_395":458,"_439":458,"_440":459,"_442":443},{"_395":456,"_439":456,"_440":457,"_442":443},{"_395":454,"_439":454,"_440":455,"_442":443},{"_395":452,"_439":452,"_440":453,"_442":443},{"_395":450,"_439":450,"_440":451,"_442":443},{"_395":448,"_439":448,"_440":449,"_442":443},{"_395":446,"_439":446,"_440":447,"_442":443},{"_395":438,"_439":438,"_440":441,"_442":443},"TAO Toolkit","value","filter",{"_444":445,"_439":438},"category","label","field","framework","TAO",{"_444":443,"_439":446},"Object Detection and Tracking in 3D",{"_444":443,"_439":448},"NSPECT-XT56-057L",{"_444":443,"_439":450},"Multi-Camera Network",{"_444":443,"_439":452},"False",{"_444":443,"_439":454},"Deep Learning",{"_444":443,"_439":456},"CV",{"_444":443,"_439":458},"Computer Vision","soln_computer_vision",{"_444":463,"_439":461},"technology","governingTerms","

Governing Terms: Use of this model is governed by the NVIDIA Open Model License.

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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","nv.referrerHref","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. 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These cookies are required for the site to function and cannot be turned off.",[1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293,1294,1295,1296,1297,1298,1299,1300,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1314,1315,1316,1317,1318,1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329],[1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117],{"_585":1236,"_395":1236,"_160":161,"_558":1237},{"_585":1230,"_395":1230,"_160":161,"_558":1231},{"_585":1218,"_395":1218,"_160":161,"_558":1219},{"_585":1215,"_395":1215,"_160":161,"_558":1216},{"_585":1212,"_395":1212,"_160":161,"_558":1213},{"_585":1207,"_395":1207,"_160":161,"_558":1208},{"_585":1202,"_395":1202,"_160":161,"_558":1203},{"_585":660,"_395":660,"_160":161,"_558":1199},{"_585":1195,"_395":1195,"_160":161,"_558":1196},{"_585":648,"_395":648,"_160":161,"_558":1193},{"_585":643,"_395":643,"_160":161,"_558":1191},{"_585":1186,"_395":1186,"_160":161,"_558":1187},{"_585":1181,"_395":1181,"_160":161,"_558":1182},{"_585":1176,"_395":1176,"_160":161,"_558":1177},{"_585":1173,"_395":1173,"_160":161,"_558":1174},{"_585":617,"_395":617,"_160":161,"_558":1171},{"_585":1163,"_395":1163,"_160":161,"_558":1164},{"_585":793,"_395":793,"_160":161,"_558":1157},{"_585":1151,"_395":1151,"_160":161,"_558":1152},{"_585":1147,"_395":1147,"_160":161,"_558":1148},{"_585":1142,"_395":1142,"_160":161,"_558":1143},{"_585":1138,"_395":1138,"_160":161,"_558":1139},{"_585":1133,"_395":1133,"_160":161,"_558":1134},{"_585":788,"_395":788,"_160":161,"_558":1126},{"_585":1118,"_395":1118,"_160":161,"_558":1119},"pexels.com",[1120,1121],{"_76":1124,"_591":1118,"_592":30,"_160":1125},{"_76":1122,"_591":1118,"_592":84,"_160":1123},"_cfuvid","Pexels provides high quality and completely free stock photos licensed under the Pexels license. 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The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[1149],{"_76":1124,"_591":1147,"_592":30,"_160":1150},"The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.","nvidiacorp.us-5.evergage.com",[1153,1154],{"_76":1156,"_591":1151,"_592":30,"_160":626},{"_76":1155,"_591":1151,"_592":30,"_160":626},"AWSALBTG","AWSALBTGCORS",[1158,1159],{"_76":1161,"_591":793,"_592":30,"_160":1162},{"_76":1160,"_591":793,"_592":30,"_160":626},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.","app.hushly.com",[1165,1166,1167],{"_76":1170,"_591":1163,"_592":30,"_160":626},{"_76":1169,"_591":1163,"_592":84,"_160":626},{"_76":1168,"_591":1163,"_592":30,"_160":626},"AWSALBCORS","JSESSIONID","AWSALB",[1172],{"_76":1124,"_591":617,"_592":30,"_160":1125},"hcaptcha.com",[1175],{"_76":1124,"_591":1173,"_592":30,"_160":1125},"tracking.intentsify.io",[1178],{"_76":1179,"_591":1176,"_592":30,"_160":1180},"userId","Used to track usage of the embedded video player.","static.reo.dev",[1183],{"_76":1184,"_591":1181,"_592":30,"_160":1185},"cookietest","Common cookie name could have a number of different origins. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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