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2.0

Introduction

  • Overview
    • NVIDIA Transfer Learning Toolkit
    • Pre-trained Models
    • TLT Workflow Overview
  • Requirements and Installation
    • Hardware Requirements
      • Minimum
      • Recommended
    • Software Requirements
    • Installation Prerequisites
    • Installation
      • Running the Transfer Learning Toolkit
        • Run the toolkit
        • Access local directories
        • Use the examples
      • Downloading the Models
        • Configure the NGC API key
        • Get a list of models
        • Download a model
  • Supported Model Architectures
    • Model Requirements
      • Classification
      • Object Detection
        • DetectNet_v2
        • FasterRCNN
        • SSD
        • DSSD
        • YOLOv3
        • RetinaNet
      • Instance Segmentation
        • MaskRCNN
      • Training
      • Deployment
  • Purpose-Built Models
    • Training
    • Deployment
      • TrafficCamNet
      • PeopleNet
      • DashCamNet
      • FaceDetect-IR
      • VehicleMakeNet
      • VehicleTypeNet
  • QAT and AMP for Training

TLT Workflow

  • Augmenting a Dataset
    • Configuring the Augmentor
      • Spatial Augmentation Config
        • Rotation Config
        • Shear Config
        • Flip Config
        • Translation Config
      • Color Augmentation Config
        • Hue Saturation Config
        • Brightness Config
        • Contrast Config
      • Dataloader
      • Blur Config
    • Running the Augmentor Tool
  • Preparing the Input Data Structure
    • Data Input for Classification
    • Data Input for Object Detection
      • KITTI file format
      • Label Files
      • Sequence Mapping File
    • Conversion to TFRecords
      • Configuration File for Dataset Converter
      • Sample Usage of the Dataset Converter Tool
    • Data Input for Instance Segmentation
      • COCO format for Instance Segmentation
  • Creating an Experiment Spec File
    • Specification File for Classification
      • Model Config
      • Eval Config
      • Training Config
    • Specification File for DetectNet_v2
      • Model Config
      • BBox Ground Truth Generator
      • Post processor
      • Cost Function
      • Trainer
      • Augmentation Module
      • Configuring the Evaluator
      • Dataloader
      • Specification File for Inference
        • Inferencer
        • TLT_Config
        • Bbox Handler
    • Specification File for FasterRCNN
      • Network Config
      • Training Configuration
    • Specification File for SSD
      • Training Config
      • Evaluation Config
      • NMS Config
      • Augmentation Config
      • Dataset Config
      • SSD config
    • Specification File for DSSD
      • Training Config
      • Evaluation Config
      • NMS Config
      • Augmentation Config
      • Dataset Config
      • DSSD Config
        • Using aspect_ratios_global or aspect_ratios
        • two_boxes_for_ar1
        • scales or Combination of min_scale and max_scale
        • clip_boxes
        • loss_loc_weight
        • focal_loss_alpha and focal_loss_gamma
        • variances
        • steps
        • offsets
        • arch
        • nlayers
        • freeze_bn
        • freeze_blocks
    • Specification File for RetinaNet
      • Training Config
      • Evaluation Config
      • NMS Config
      • Augmentation Config
      • Dataset Config
      • RetinaNet Config
    • Specification File for YOLOv3
      • Training Config
      • Evaluation Config
      • NMS Config
      • Augmentation Config
      • Dataset Config
      • YOLOv3 Config
    • Specification File for MaskRCNN
      • MaskRCNN Config
      • Data Config
  • Training the Model
    • Quantization Aware Training
    • Automatic Mixed Precision
    • Training a classification model
      • Required Arguments
      • Optional Arguments
    • Training a DetectNet_v2 model
      • Required Arguments
      • Optional Arguments
      • Sample Usage
    • Training a FasterRCNN Model
      • Required Arguments
      • Optional Arguments
      • Sample Usage
      • Using a Pretrained Weights File
    • Training an SSD Model
      • Required Arguments
      • Optional Arguments
      • Sample Usage
    • Training a DSSD Model
      • Required arguments
      • Optional Arguments
      • Sample Usage
    • Training a YOLOv3 Model
      • Required Arguments
      • Optional Arguments
      • Sample Usage
    • Training a RetinaNet model
      • Required Arguments
      • Optional Arguments
      • Sample Usage
    • Training a MaskRCNN Model
      • Required Arguments
      • Optional Arguments
      • Sample Usage
  • Evaluating the Model
    • Evaluating a Classification Model
      • Required Arguments
      • Optional Arguments
    • Evaluating a DetectNet_v2 Model
      • Required Arguments
      • Optional Arguments
    • Evaluating a FasterRCNN Model
      • Required Arguments
      • Optional Arguments
      • Evaluation Metrics
      • Two Modes for tlt-evaluate
    • Evaluating an SSD Model
      • Required Arguments
      • Optional Arguments
    • Evaluating a DSSD model
      • Required Arguments
      • Optional Arguments
    • Evaluating a YOLOv3 Model
      • Required Arguments
      • Optional Arguments
    • Evaluating a RetinaNet Model
      • Required Arguments
      • Optional Arguments
    • Evaluating a MaskRCNN Model
      • Required Arguments
      • Optional Arguments
  • Using Inference on a Model
    • Running Inference on a Classification Model
      • Required arguments
      • Optional arguments
    • Running Inference on a DetectNet_v2 Model
      • Required Parameters
    • Running Inference on a FasterRCNN Model
      • Required Arguments
      • Optional Arguments
      • Two Modes for tlt-infer
    • Running Inference on an SSD Model
      • Required Arguments
      • Optional Arguments
    • Running Inference on a DSSD Model
      • Required Arguments
      • Optional Arguments
    • Running Inference on a YOLOv3 Model
      • Required Arguments
      • Optional Arguments
    • Running Inference on a RetinaNet Model
      • Required Arguments
      • Optional Arguments
      • Required Arguments
      • Optional Arguments
      • Required Arguments
      • Optional arguments
    • Running Inference on a MaskRCNN Model
      • Required Arguments
      • Optional Arguments
      • Required Arguments
      • Optional Arguments
  • Pruning the Model
    • Required Arguments
    • Optional Arguments
    • Using the Prune Command
    • Re-training the Pruned Model
  • Exporting the Model
    • INT8 Mode Overview
    • FP16/FP32 Model
    • Generating an INT8 tensorfile Using the tlt-int8-tensorfile Command
      • Positional Arguments
      • Required Arguments
      • Optional Argument
    • Exporting the Model Using tlt-export
      • Required Arguments
      • Optional Arguments
    • INT8 Export Mode Required Arguments
    • INT8 Export Optional Arguments
    • Exporting a Model
  • Deploying to Deepstream
    • TensorRT Open Source Software (OSS)
      • TensorRT OSS on x86
      • TensorRT OSS on Jetson (ARM64)
    • Generating an Engine Using tlt-converter
      • Instructions for x86
      • Instructions for Jetson
      • Using the tlt-converter
        • Required Arguments
        • Optional Arguments
        • INT8 Mode Arguments
        • Sample Output Log
    • Integrating the model to DeepStream
      • Integrating a Classification model
        • Label File
        • DeepStream Configuration File
      • Integrating a DetectNet_v2 model
        • Label File
        • DeepStream Configuration File
      • Integrating an SSD Model
        • Prerequisites for SSD Model
        • Label File
        • DeepStream Configuration File
      • Integrating a FasterRCNN Model
        • Prerequisite for FasterRCNN Model
        • Label File
        • DeepStream Configuration File
      • Integrating a YOLOv3 Model
        • Prerequisite for YOLOv3 model
        • Label File
        • DeepStream Configuration File
      • Integrating a DSSD Model
        • Prerequisite for DSSD model
        • Label File
        • DeepStream Configuration File
      • Integrating a RetinaNet Model
        • Prerequisite for RetinaNet Model
        • Label File
        • DeepStream Configuration File
      • Integrating Purpose-Built Models
      • Integrating a MaskRCNN Model
        • deepstream-app Config File
        • Label File

More Information

  • Release Notes
    • Transfer Learning Toolkit for Intelligent Video Analytics V2.0
      • Key Features
      • Contents
      • Software Requirements
      • Hardware Requirements
        • Minimum
        • Recommended
      • Known Issues
      • Resolved Issues
  • Frequently Asked Questions
    • Model support
    • Pruning
    • Model Export and Deployment
    • Training
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