google-research/deeplab2

Health v2 · maintenance only

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1037 stars · 164 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DeepLab2 is a TensorFlow library from Google Research providing a unified, state-of-the-art codebase for dense pixel labeling tasks such as semantic, instance, and panoptic segmentation, depth estimation, and video panoptic segmentation. It includes research models like MaX-DeepLab, kMaX-DeepLab, ViP-DeepLab, and MOAT with pretrained checkpoints.

Use cases

• train a panoptic segmentation model on Cityscapes or COCO
• run semantic segmentation inference on images with pretrained checkpoints
• estimate per-pixel depth from images
• perform video panoptic segmentation on autonomous driving datasets like KITTI-STEP or Waymo
• fine-tune kMaX-DeepLab or ViP-DeepLab on a custom segmentation dataset
• evaluate segmentation models with metrics like Panoptic Quality or Segmentation and Tracking Quality

When to choose

• you need state-of-the-art dense pixel labeling models in TensorFlow
• you want a unified codebase covering multiple segmentation and depth estimation tasks
• you are doing research on panoptic or video panoptic segmentation
• you need pretrained model zoo checkpoints for segmentation research

When to avoid

• you prefer PyTorch over TensorFlow
• you need a lightweight production inference library rather than a research codebase
• your task is not pixel-level prediction (e.g., classification or detection only)
• you need actively maintained software with frequent updates

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision machine-learning deep-learning image-processing python tensorflow semantic-segmentation panoptic-segmentation instance-segmentation depth-estimation video-panoptic-segmentation pixel-labeling model-zoo gpu linux

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markdown · JSON · MCP: product_card(name="google-research/deeplab2")