detectron2
latest
  • Tutorials
    • Installation
    • Getting Started with Detectron2
    • Use Builtin Datasets
    • Extend Detectron2’s Defaults
    • Use Custom Datasets
    • Dataloader
    • Data Augmentation
    • Use Models
    • Write Models
    • Training
    • Evaluation
    • Yacs Configs
    • Lazy Configs
    • Deployment
  • Notes
  • API Documentation
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Tutorials¶

  • Installation
    • Requirements
    • Build Detectron2 from Source
    • Install Pre-Built Detectron2 (Linux only)
    • Common Installation Issues
    • Installation inside specific environments:
  • Getting Started with Detectron2
    • Inference Demo with Pre-trained Models
    • Training & Evaluation in Command Line
    • Use Detectron2 APIs in Your Code
  • Use Builtin Datasets
    • Expected dataset structure for COCO instance/keypoint detection:
    • Expected dataset structure for PanopticFPN:
    • Expected dataset structure for LVIS instance segmentation:
    • Expected dataset structure for cityscapes:
    • Expected dataset structure for Pascal VOC:
    • Expected dataset structure for ADE20k Scene Parsing:
  • Extend Detectron2’s Defaults
  • Use Custom Datasets
    • Register a Dataset
    • “Metadata” for Datasets
    • Register a COCO Format Dataset
    • Update the Config for New Datasets
  • Dataloader
    • How the Existing Dataloader Works
    • Write a Custom Dataloader
    • Use a Custom Dataloader
  • Data Augmentation
    • Basic Usage
    • Write New Augmentations
    • Advanced Usage
  • Use Models
    • Build Models from Yacs Config
  • Write Models
    • Register New Components
    • Construct Models with Explicit Arguments
  • Training
    • Custom Training Loop
    • Trainer Abstraction
    • Logging of Metrics
  • Evaluation
    • Use evaluators
    • Evaluators for custom dataset
  • Yacs Configs
    • Basic Usage
    • Configs in Projects
    • Best Practice with Configs
  • Lazy Configs
    • Python Syntax
    • Recursive Instantiation
    • Using Model Zoo LazyConfigs
    • Summary
  • Deployment
    • Deployment with Tracing or Scripting
    • Deployment with Caffe2-tracing
    • Conversion to TensorFlow
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