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name: vision-and-apps description: "Routes TensorLayer pretrained vision models, image apps, and visual inference workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Vision and Apps

Use this sub-skill for TensorLayer pretrained image constructors, object-detection and pose wrappers, spatial-transformer patterns, and image-centric application tutorials. This is the route for vision workflows that sit above the core model APIs.

Typical requests

• Instantiate VGG16, MobileNetV1, ResNet50, or SqueezeNetV1.
• Inspect YOLOv4 or human-pose wrapper behavior.
• Understand image inference, drawing, or visualization workflows.
• Adapt a pretrained image example to a safe local smoke.

Read first

• references/model-overview.md for constructor and wrapper notes.
• references/workflows.md for tiny vision smoke patterns.
• references/troubleshooting.md for missing weights, OpenCV, and headless display issues.

Bundled check

• scripts/smoke_vision_models.py instantiates the main image constructors with pretrained=False and can optionally run a tiny forward pass.

Boundaries

Include here:

• tensorlayer.app
• pretrained image constructors in tensorlayer.models
• object detection, pose estimation, and image app tutorials
• spatial-transformer and quantized image workflows when they are vision-centric

Exclude or route elsewhere:

• core serialization or layer mechanics -> core-modeling
• generic data loading or preprocessing helpers -> data-and-utilities
• training-loop orchestration and CLI help -> training-and-cli
• text or RL workflows -> text-and-sequence / reinforcement-learning

Fast path

1. Decide whether the request is an image model, app wrapper, or visualization task.
2. Keep pretrained constructors on pretrained=False unless the user explicitly needs weights.
3. Use the smoke script before opening a large tutorial or external weight path.