TAO Toolkit v5.3.0
TAO Toolkit v5.3.0


MaskRCNN supports the following tasks:

  • dataset_convert

  • train

  • evaluate

  • prune

  • inference

  • export

These tasks may be invoked from the TAO Toolkit Launcher using the following convention on the command line:


tao model mask_rcnn <sub_task> <args_per_subtask>

where args_per_subtask are the command-line arguments required for a given subtask. Each of these subtasks are explained in detail below.

The raw image data and the corresponding annotation file need to be converted to TFRecords before training and evaluation. The dataset_convert tool helps to achieve seamless conversion while providing insight on potential issues in an annotation file. The following sections detail how to use dataset_convert.

Sample Usage of the Dataset Converter Tool

The dataset_convert tool is described below:


tao model mask_rcnn dataset-convert [-h] -i <image_directory> -a <annotation_json_file> -o <tfrecords_output_directory> [-t <tag>] [-s <num_shards>] [--include_mask]

You can use the following arguments:

  • -i, --image_dir: The path to the directory where raw images are stored

  • -a, --annotations_file: The annotation JSON file

  • -o, --output_dir: The output directory where TFRecords are saved

  • -t, --tag: The tag for the converted TFRecords (e.g. “train”). The tag defaults to the name of the annotation file.

  • -s, --num_shards: The number of shards for the converted TFRecords. The default value is 256.

  • --include_mask: Whether to include segmentation groundtruth during conversion. The default value is True.

  • -h, --help: Show this help message and exit.


    A log file named <tag>_warnings.json will be generated in the output_dir if the bounding box of an object is out of bounds with respect to the image frame or if an object mask is out of bounds with respect to its bounding box. The log file records the image_id that has problematic object IDs. For example, {"200365": {"box": [918], "mask": []} means the bounding box of object 918 is out of bounds in image 200365.

The following example shows how to use the command with the dataset:


tao model mask_rcnn dataset_convert -i /path/to/image_dir -a /path/to/train.json -o /path/to/output_dir


The id under categories in the annotation file should start from 1.

Below is a sample MaskRCNN spec file. It has three major components: top level experiment configs, data_config, and maskrcnn_config, explained below in detail. The format of the spec file is a protobuf text (prototxt) message and each of its fields can be either a basic data type or a nested message. The top level structure of the spec file is summarized in the table below.

Here’s a sample of the MaskRCNN spec file:


seed: 123 use_amp: False warmup_steps: 0 checkpoint: "/workspace/tao-experiments/maskrcnn/pretrained_resnet50/tlt_instance_segmentation_vresnet50/resnet50.hdf5" learning_rate_steps: "[60000, 80000, 100000]" learning_rate_decay_levels: "[0.1, 0.02, 0.002]" total_steps: 120000 train_batch_size: 2 eval_batch_size: 4 num_steps_per_eval: 10000 momentum: 0.9 l2_weight_decay: 0.0001 l1_weight_decay: 0.0 warmup_learning_rate: 0.0001 init_learning_rate: 0.02 num_examples_per_epoch: 14700 # pruned_model_path: "/workspace/tao-experiments/maskrcnn/pruned_model/model.tlt" data_config{ image_size: "(832, 1344)" augment_input_data: True eval_samples: 500 training_file_pattern: "/workspace/tao-experiments/data/train*.tfrecord" validation_file_pattern: "/workspace/tao-experiments/data/val*.tfrecord" val_json_file: "/workspace/tao-experiments/data/annotations/instances_val2017.json" # dataset specific parameters num_classes: 91 skip_crowd_during_training: True max_num_instances: 200 } maskrcnn_config { nlayers: 50 arch: "resnet" freeze_bn: True freeze_blocks: "[0,1]" gt_mask_size: 112 # Region Proposal Network rpn_positive_overlap: 0.7 rpn_negative_overlap: 0.3 rpn_batch_size_per_im: 256 rpn_fg_fraction: 0.5 rpn_min_size: 0. # Proposal layer. batch_size_per_im: 512 fg_fraction: 0.25 fg_thresh: 0.5 bg_thresh_hi: 0.5 bg_thresh_lo: 0. # Faster-RCNN heads. fast_rcnn_mlp_head_dim: 1024 bbox_reg_weights: "(10., 10., 5., 5.)" # Mask-RCNN heads. include_mask: True mrcnn_resolution: 28 # training train_rpn_pre_nms_topn: 2000 train_rpn_post_nms_topn: 1000 train_rpn_nms_threshold: 0.7 # evaluation test_detections_per_image: 100 test_nms: 0.5 test_rpn_pre_nms_topn: 1000 test_rpn_post_nms_topn: 1000 test_rpn_nms_thresh: 0.7 # model architecture min_level: 2 max_level: 6 num_scales: 1 aspect_ratios: "[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]" anchor_scale: 8 # localization loss rpn_box_loss_weight: 1.0 fast_rcnn_box_loss_weight: 1.0 mrcnn_weight_loss_mask: 1.0 }

Field Description Data Type and Constraints Recommended/Typical Value
seed The random seed for the experiment Unsigned int 123
warmup_steps The steps taken for learning rate to ramp up to the init_learning_rate Unsigned int
warmup_learning_rate The initial learning rate during the warmup phase float
learning_rate_steps A list of steps at which the learning rate decays by the factor specified in learning_rate_decay_levels string
learning_rate_decay_levels A list of decay factors. The length should match the length of learning_rate_steps. string
total_steps The total number of training iterations Unsigned int
train_batch_size The batch size during training Unsigned int 4
eval_batch_size The batch size during validation or evaluation Unsigned int 8
num_steps_per_eval Save a checkpoint and run evaluation every N steps. Unsigned int
momentum Momentum of the SGD optimizer float 0.9
l1_weight_decay L1 weight decay float 0.0001
l2_weight_decay L2 weight decay float 0.0001
use_amp Specifies whether to use Automatic Mixed Precision training boolean False
checkpoint The path to a pretrained model string
maskrcnn_config The architecture of the model message
data_config The input data configuration message
skip_checkpoint_variables If specified, the weights of the layers with matching regular expressions will not be loaded. This is especially helpful for transfer learning. string
pruned_model_path The path to a pruned MaskRCNN model string
num_examples_per_epoch The total number of images in the training set divided by the number of GPUs Unsigned int
num_epochs The number of epochs to train the network Unsigned int
visualize_images_summary Whether to preview images and inference results in TensorBoard. If enabled, 10 images with predicted bounding boxes from the validation set will be saved in the TensorBoard events file. boolean

The num_examples_per_epoch parameter must be specified, and either num_epochs or total_steps should be specified. If both are set, total_steps will be overwritten by num_epochs * num_examples_per_epoch / train_batch_size


When using skip_checkpoint_variables, you can first find the model structure in the training log (Part of the MaskRCNN+ResNet50 model structure is shown below). If, for example, you want to retrain all prediction heads, you can set skip_checkpoint_variables to “head”. TAO Toolkit uses the Python re library to check whether “head” matches any layer name or re.search($skip_checkpoint_variables, $layer_name).


[MaskRCNN] INFO : ================ TRAINABLE VARIABLES ================== [MaskRCNN] INFO : [#0001] conv1/kernel:0 => (7, 7, 3, 64) [MaskRCNN] INFO : [#0002] bn_conv1/gamma:0 => (64,) [MaskRCNN] INFO : [#0003] bn_conv1/beta:0 => (64,) [MaskRCNN] INFO : [#0004] block_1a_conv_1/kernel:0 => (1, 1, 64, 64) [MaskRCNN] INFO : [#0005] block_1a_bn_1/gamma:0 => (64,) [MaskRCNN] INFO : [#0006] block_1a_bn_1/beta:0 => (64,) [MaskRCNN] INFO : [#0007] block_1a_conv_2/kernel:0 => (3, 3, 64, 64) [MaskRCNN] INFO : [#0008] block_1a_bn_2/gamma:0 => (64,) [MaskRCNN] INFO : [#0009] block_1a_bn_2/beta:0 => (64,) [MaskRCNN] INFO : [#0010] block_1a_conv_3/kernel:0 => (1, 1, 64, 256) [MaskRCNN] INFO : [#0011] block_1a_bn_3/gamma:0 => (256,) [MaskRCNN] INFO : [#0012] block_1a_bn_3/beta:0 => (256,) [MaskRCNN] INFO : [#0110] block_3d_bn_3/gamma:0 => (1024,) [MaskRCNN] INFO : [#0111] block_3d_bn_3/beta:0 => (1024,) [MaskRCNN] INFO : [#0112] block_3e_conv_1/kernel:0 => (1, 1, 1024, [MaskRCNN] INFO : [#0144] block_4b_bn_1/beta:0 => (512,) … … … … ... [MaskRCNN] INFO : [#0174] post_hoc_d5/kernel:0 => (3, 3, 256, 256) [MaskRCNN] INFO : [#0175] post_hoc_d5/bias:0 => (256,) [MaskRCNN] INFO : [#0176] rpn/kernel:0 => (3, 3, 256, 256) [MaskRCNN] INFO : [#0177] rpn/bias:0 => (256,) [MaskRCNN] INFO : [#0178] rpn-class/kernel:0 => (1, 1, 256, 3) [MaskRCNN] INFO : [#0179] rpn-class/bias:0 => (3,) [MaskRCNN] INFO : [#0180] rpn-box/kernel:0 => (1, 1, 256, 12) [MaskRCNN] INFO : [#0181] rpn-box/bias:0 => (12,) [MaskRCNN] INFO : [#0182] fc6/kernel:0 => (12544, 1024) [MaskRCNN] INFO : [#0183] fc6/bias:0 => (1024,) [MaskRCNN] INFO : [#0184] fc7/kernel:0 => (1024, 1024) [MaskRCNN] INFO : [#0185] fc7/bias:0 => (1024,) [MaskRCNN] INFO : [#0186] class-predict/kernel:0 => (1024, 91) [MaskRCNN] INFO : [#0187] class-predict/bias:0 => (91,) [MaskRCNN] INFO : [#0188] box-predict/kernel:0 => (1024, 364) [MaskRCNN] INFO : [#0189] box-predict/bias:0 => (364,) [MaskRCNN] INFO : [#0190] mask-conv-l0/kernel:0 => (3, 3, 256, 256) [MaskRCNN] INFO : [#0191] mask-conv-l0/bias:0 => (256,) [MaskRCNN] INFO : [#0192] mask-conv-l1/kernel:0 => (3, 3, 256, 256) [MaskRCNN] INFO : [#0193] mask-conv-l1/bias:0 => (256,) [MaskRCNN] INFO : [#0194] mask-conv-l2/kernel:0 => (3, 3, 256, 256) [MaskRCNN] INFO : [#0195] mask-conv-l2/bias:0 => (256,) [MaskRCNN] INFO : [#0196] mask-conv-l3/kernel:0 => (3, 3, 256, 256) [MaskRCNN] INFO : [#0197] mask-conv-l3/bias:0 => (256,) [MaskRCNN] INFO : [#0198] conv5-mask/kernel:0 => (2, 2, 256, 256) [MaskRCNN] INFO : [#0199] conv5-mask/bias:0 => (256,) [MaskRCNN] INFO : [#0200] mask_fcn_logits/kernel:0 => (1, 1, 256, 91) [MaskRCNN] INFO : [#0201] mask_fcn_logits/bias:0 => (91,)

MaskRCNN Config

The MaskRCNN configuration (maskrcnn_config) defines the model structure. This model is used for training, evaluation, and inference. A detailed description is included in the table below. Currently, MaskRCNN only supports ResNet10/18/34/50/101 as its backbone.

Field Description Data Type and Constraints Recommended/Typical Value
nlayers The number of layers in ResNet arch Unsigned int 50
arch The backbone feature extractor name string resnet
freeze_bn Whether to freeze all BatchNorm layers in the backbone boolean False
freeze_blocks A list of conv blocks in the backbone to freeze string ResNet: For the ResNet series, the block IDs valid for freezing are any subset of [0, 1, 2, 3] (inclusive)
gt_mask_size The groundtruth mask size Unsigned int 112
rpn_positive_overlap The lower-bound threshold to assign positive labels for anchors float 0.7
rpn_negative_overlap The upper-bound threshold to assign negative labels for anchors float 0.3
rpn_batch_size_per_im The number of sampled anchors per image in RPN Unsigned int 256
rpn_fg_fraction The desired fraction of positive anchors in a batch Unsigned int 0.5
rpn_min_size The minimum proposal height and width 0
batch_size_per_im The RoI minibatch size per image Unsigned int 512
fg_fraction The target fraction of RoI minibatch that is labeled as foreground float 0.25
fast_rcnn_mlp_head_dim The Fast-RCNN classification head dimension Unsigned int 1024
bbox_reg_weights The bounding-box regularization weights string “(10, 10, 5, 5)”
include_mask Specifies whether to include a mask head boolean True (currently only True is supported)
mrcnn_resolution The mask-head resolution (must be multiple of 4) Unsigned int 28
train_rpn_pre_nms_topn The number of top-scoring RPN proposals to keep before applying NMS (per FPN level) during training Unsigned int 2000
train_rpn_post_nms_topn The number of top-scoring RPN proposals to keep after applying NMS (total number produced) during training Unsigned int 1000
train_rpn_nms_threshold The NMS IOU threshold in RPN during training float 0.7
test_detections_per_image The number of bounding box candidates after NMS Unsigned int 100
test_nms The NMS IOU threshold during test float 0.5
test_rpn_pre_nms_topn The number of top-scoring RPN proposals to keep before applying NMS (per FPN level) during test Unsigned int 1000
test_rpn_post_nms_topn The number of top scoring RPN proposals to keep after applying NMS (total number produced) during test Unsigned int 1000
test_rpn_nms_threshold The NMS IOU threshold in RPN during test float 0.7
min_level The minimum level of the output feature pyramid Unsigned int 2
max_level The maximum level of the output feature pyramid Unsigned int 6
num_scales The number of anchor octave scales on each pyramid level (e.g. if set to 3, the anchor scales are [2^0, 2^(1/3), 2^(2/3)]) Unsigned int 1
aspect_ratios A list of tuples representing the aspect ratios of anchors on each pyramid level string “[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]”
anchor_scale Scale of the base-anchor size to the feature-pyramid stride Unsigned int 8
rpn_box_loss_weight The weight for adjusting RPN box loss in the total loss float 1.0
fast_rcnn_box_loss_weight The weight for adjusting FastRCNN box regression loss in the total loss float 1.0
mrcnn_weight_loss_mask The weight for adjusting mask loss in the total loss float 1.0

The min_level, max_level, num_scales, aspect_ratios, and anchor_scale are used to determine anchor generation for MaskRCNN. anchor_scale is the base anchor scale, while min_level and max_level set the range of the scales on different feature maps. For example, the actual anchor scale for the feature map at min_level will be anchor_scale * 2^min_level and the actual anchor scale for the feature map at max_level will be anchor_scale * 2^max_level. And it will generate anchors of different aspect_ratios based on the actual anchor scale.

Data Config

The data configuration (data_config) specifies the input data source and format. This is used for training, evaluation, and inference. A detailed description is summarized in the table below.

Field Description Data Type and Constraints Recommended/Typical Value
image_size The image dimension as a tuple within quote marks. “(height, width)” indicates the dimension of the resized and padded input. string “(832, 1344)”
augment_input_data Specifies whether to augment the data boolean True
eval_samples The number of samples for evaluation Unsigned int
training_file_pattern The TFRecord path for training string
validation_file_pattern The TFRecord path for validation string
val_json_file The annotation file path for validation string
num_classes The number of classes. If there are N categories in the annotation, num_classes should be N+1 (background class) Unsigned int
skip_crowd_during_training Specifies whether to skip crowd during training boolean True
prefetch_buffer_size The prefetch buffer size used by tf.data.Dataset (default: AUTOTUNE) Unsigned int
shuffle_buffer_size The shuffle buffer size used by tf.data.Dataset (default: 4096) Unsigned int 4096
n_workers The number of workers to parse and preprocess data (default: 16) Unsigned int 16
max_num_instances The maximum number of object instances to parse (default: 200) Unsigned int 200

If an out-of-memory error occurs during training, try to set a smaller image_size or batch_size first. If the error persists, try reducing the n_workers, shuffle_buffer_size, and prefetch_buffer_size values. Lastly, if the original images have a very large resolution, resize the images offline and create new tfrecords to avoid loading large images to GPU memory.

Train the MaskRCNN model using this command:


tao model mask_rcnn train [-h] -e <experiment_spec> -d <output_dir> -k <key> [--gpus <num_gpus>] [--gpu_index <gpu_index>] [--log_file <log_file_path>]

Required Arguments

  • -d, --model_dir: The path to the folder where the experiment output is written.

  • -k, --key: The encryption key to decrypt the model.

  • -e, --experiment_spec_file: The experiment specification file to set up the evaluation. experiment. This should be the same as the training specification file.

Optional Arguments

  • --gpus num_gpus: The number of GPUs to use and processes to launch for training. The default value is 1.

  • --gpu_index: The index of the (discrete) GPU for exporting the model if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

  • --log_file: The path to the log file. The default path is stdout.

  • -h, --help: Show this help message and exit.

Input Requirement

  • Input size: C * W * H (where C = 3, W >= 128, H >= 128 and W, H are multiples of 2^ max_level)

  • Image format: JPG

  • Label format: COCO detection

Sample Usage

Here’s an example of using the train command on a MaskRCNN model:


tao model mask_rcnn train --gpus 2 -e /path/to/spec.txt -d /path/to/result -k $KEY

To run evaluation for a MaskRCNN model, use this command:


tao model mask_rcnn evaluate [-h] -e <experiment_spec_file> -m <model_file> -k <key> [--gpu_index <gpu_index>] [--log_file <log_file_path>]

Required Arguments

  • -e, --experiment_spec_file: The experiment spec file to set up the evaluation experiment. This should be the same as the training spec file.

  • -m, --model: The path to the model file to use for evaluation (only .tlt model is supported).

  • -k, --key: The key to load the model.

Optional Arguments

  • --gpu_index: The index of the (discrete) GPU for exporting the model if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

  • --log_file: The path to the log file. The default path is stdout.

  • -h, --help: Show this help message and exit.

Pruning removes parameters from the model to reduce the model size. Retraining is necessary to regain the performance of the unpruned model.

The prune command includes these parameters:


tao model mask_rcnn prune [-h] -m <pretrained_model> -o <output_dir> -k <key> [-n <normalizer>] [-eq <equalization_criterion>] [-pg <pruning_granularity>] [-pth <pruning threshold>] [-nf <min_num_filters>] [-el [<excluded_list>] [--gpu_index <gpu_index>] [--log_file <log_file>]

Required Arguments

  • -m, --pretrained_model: The path to the pretrained model.

  • -o, --output_dir: The output directory which contains the pruned model, named as model.tlt.

  • -k, --key: The key to load a .tlt model.

Optional Arguments

  • -h, --help: Show this help message and exit.

  • -n, --normalizer: max to normalize by dividing each norm by the maximum norm within a layer; L2 to normalize by dividing by the L2 norm of the vector comprising all kernel norms. (default: max)

  • -eq, --equalization_criterion: Criteria to equalize the stats of inputs to an element wise op layer, or depth-wise convolutional layer. This parameter is useful for resnets and mobilenets. Options are arithmetic_mean, geometric_mean, union, and intersection. (default: union)

  • -pg, --pruning_granularity: Number of filters to remove at a time. (default:8)

  • -pth: Threshold to compare normalized norm against. (default:0.1)

  • -nf, --min_num_filters: Minimum number of filters to keep per layer (default:16)

  • -el, --excluded_layers: List of excluded_layers. Examples: -i item1 item2 (default: [])

  • --gpu_index: The index of the GPU to run evaluation (useful when the machine has multiple GPUs installed). Note that evaluation can only run on a single GPU.

  • --log_file: The path to the log file. Defaults to stdout.

Here’s an example of using the prune command:


tao model mask_rcnn prune -m /workspace/model.step-100.tlt -o /workspace/output -eq union -pth 0.7 -k $KEY

After pruning, the model needs to be retrained first before it can be used for inference or evaluation.

Once the model has been pruned, there might be a decrease in accuracy. This happens because some previously useful weights may have been removed. To regain accuracy, NVIDIA recommends that you retrain this pruned model over the same dataset. To do this, run the tao model mask_rcnn train command with an updated spec file that points to the newly pruned model by setting pruned_model_path.

Users are advised to turn off the regularizer during retraining. You may do this by setting the regularizer weights to 0 for both l1_weight_decay and l2_weight_decay. The other parameters may be retained in the spec file from the previous training. train_batch_size and eval_batch_size must be kept unchanged.

The inference tool for MaskRCNN networks can be used to visualize bboxes or generate frame-by-frame COCO-format labels on a directory of images. Here’s an example of using this tool:


tao model mask_rcnn inference [-h] -i <input directory> -r <results directory> -e <experiment spec file> -m <model file> [-k <key>] [-t <bbox confidence threshold>] [--include_mask] [--gpu_index <gpu_index>] [--log_file <log_file_path>]

Required Arguments

  • -m, --model_path: The path to the trained MaskRCNN model (either a .tlt model or a converted TensorRT engine).

  • -i, --image_dir: The directory of input images for inference. Supported image formats include PNG, JPG and JPEG.

  • -e, --experiment_spec: The path to an experiment spec file for training.

  • -r, --results_dir: The directory path to output annotated images.

Optional Arguments

  • -k, --key: The key to load a .tlt model (not required if TensorRT engine is used).

  • -t, --threshold: The threshold for drawing a bbox (default: 0.6)

  • -c, --class_map: A text file containing class names, which should match the category names in the annotation file in ascending order of category IDs. If the label file is omitted, the annotated image will not display category names next to bounding boxes. This argument is only supported with the TensorRT engine.

  • --include_mask: Specifies whether to draw masks on the annotated output.

  • --gpu_index: The index of the (discrete) GPU for exporting the model if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

  • --log_file: The path to the log file. The default path is stdout.

  • -h, --help: Show this help message and exit.

Exporting the model decouples the training process from inference and allows conversion to TensorRT engines outside the TAO environment. TensorRT engines are specific to each hardware configuration and should be generated for each unique inference environment. The exported model may be used universally across training and deployment hardware.

The exported model format is referred to as .etlt. Like the .tlt model format, .etlt is an encrypted model format, and it uses the same key as the .tlt model that it is exported from. This key is required when deploying this model.

INT8 Mode Overview

TensorRT engines can be generated in INT8 mode to improve performance, but require a calibration cache at engine creation-time. The calibration cache is generated using a calibration tensor file, if export is run with the --data_type flag set to int8. Pre-generating the calibration information and caching it removes the need for calibrating the model on the inference machine. Moving the calibration cache is usually much more convenient than moving the calibration tensorfile, since it is a much smaller file and can be moved with the exported model. Using the calibration cache also speeds up engine creation as building the cache can take several minutes to generate depending on the size of the Tensorfile and the model itself.

The export tool can generate the INT8 calibration cache by ingesting training data using one of these options:

  • Option 1: Use the training data loader to load the training images for INT8 calibration. This option is now the recommended approach to support multiple image directories by leveraging the training dataset loader. This also ensures two important aspects of data during calibration:

    • Data pre-processing in the INT8 calibration step is the same as in the training process.

    • The data batches are sampled randomly across the entire training dataset, thereby improving the accuracy of the INT8 model.

  • Option 2: Point the tool to a directory of images that you want to use to calibrate the model. For this option, make sure to create a sub-sampled directory of random images that best represent your training dataset.

FP16/FP32 Model

The calibration.bin is only required if you need to run inference at INT8 precision. For FP16/FP32-based inference, the export step is much simpler: All you need to do is provide a .tlt model from the training/retraining step to be converted into .etlt format.

Exporting the MaskRCNN Model

Here’s an example of the command line arguments of the tao model mask_rcnn export command:


tao model mask_rcnn export [-h] -m <path to the .tlt model file generated by tao model train> -k <key> --experiment_spec <path to experiment spec file> [-o <path to output file>] [--gen_ds_config <Flag to generate ds config and label file>] [--gpu_index <gpu_index>] [--log_file <log_file_path>]

Required Arguments

  • -m, --model: The path to the .tlt model file to be exported using export.

  • -k, --key: The key used to save the .tlt model file.

  • -e, --experiment_spec: The path to the spec file.

Optional Arguments

  • -o, --output_file: The path to save the exported model to. The default path is ./<input_file>.etlt.

  • --gen_ds_config: A Boolean flag indicating whether to generate the template DeepStream related configuration (“nvinfer_config.txt”) as well as a label file (“labels.txt”) in the same directory as the output_file. Note that the config file is NOT a complete configuration file and requires the user to update the sample config files in DeepStream with the parameters generated.

  • --gpu_index: The index of the (discrete) GPU for exporting the model if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

  • --log_file: The path to the log file. The default path is stdout.

  • -h, --help: Show this help message and exit.


MaskRCNN does not support QAT.

Sample usage

Here’s a sample command to export a MaskRCNN model in INT8 mode:


tao model mask_rcnn export -m /ws/model.step-25000.tlt \ -k nvidia_tlt \ -e /ws/maskrcnn_train_resnet50.txt

For TensorRT engine generation, validation, and int8 calibration, refer to the TAO Deploy documentation.

Refer to Integrating a MaskRCNN Model to learn more about deploying a MaskRCNN model to DeepStream.

Previous Data Input for Instance Segmentation
Next Mask Auto Labeler
© Copyright 2023, NVIDIA.. Last updated on May 24, 2024.