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t-rex

t-rex

Use the T-Rex2 cloud API wrapper for visual-prompt object detection, embeddings, visualization, and the optional Gradio demo.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: t-rex description: "Use the T-Rex2 cloud API wrapper for visual-prompt object detection, embeddings, visualization, and the optional Gradio demo." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

T-Rex Repo Skill

Use this skill when a task asks for T-Rex2, T-Rex visual prompts, DeepDataSpace T-Rex API calls, generic object detection from text/visual prompts, reusable visual embeddings, detection rendering, or the optional Gradio demo.

T-Rex2 in this repository is a Python cloud API wrapper, not a local model checkpoint or training framework. Live detections require a DeepDataSpace API token and network access; local GPU/accelerator hardware is not required for the selected workflows.

First checks

  1. Confirm that the task really needs T-Rex2's visual prompt cloud API or renderer.

  2. Install the package in the user's working environment. For a source checkout or source archive, use a clean Python environment, install runtime dependencies such as requests, Pillow, numpy, pydantic==2.10.6, and gradio==4.44.1, then install the package. If source metadata build fails on torch, read references/troubleshooting.md; this is a setup-time packaging quirk, not a local GPU requirement.

  3. If package availability is uncertain, run the bundled offline smoke check from this skill directory:

    python scripts/check_trex_install.py --json
    
  4. For live cloud calls, confirm the user can provide a token through --token or T_REX_API_TOKEN; do not log or persist secrets.

  5. Before refreshing this skill against a checkout, read references/repo-provenance.md.

Routes

User taskRead
Build or validate prompt JSON, call TRex2APIWrapper, run interactive/generic visual prompt detection, create visual embeddings, or run embedding inferencesub-skills/cloud-api-workflows/SKILL.md
Draw boxes or points, filter detections by score, fix detection JSON, operate/debug the optional Gradio demo, or handle score.item visualization errorssub-skills/visualization-and-demo/SKILL.md
Install/import failures, setup-time torch failures, Gradio dependency compatibility, token/network boundary, or backend questionsreferences/troubleshooting.md
Check source commit, package version, evidence paths, or stalenessreferences/repo-provenance.md

Package and dependency facts

  • Public import surface: from trex import TRex2APIWrapper, visualize.
  • Installed distribution metadata verified as trex==1.0; source trex/version.py contains v1.0.
  • Source package metadata pins pydantic==2.10.6 and gradio==4.44.1.
  • Source modules import requests, Pillow, and numpy; ensure they are available even when not named in the package requirement file.
  • setup.py imports torch.utils.cpp_extension during source installation. This is a setup-time packaging quirk, not evidence of local GPU inference.
  • The optional Gradio demo also uses gradio-image-prompter and may need huggingface_hub<1.0 with Gradio 4.44.1.

Common operating patterns

  • Use --dry-run on cloud sub-skill scripts to validate prompt images and JSON without any network call.
  • Use rectangle prompts in [x1, y1, x2, y2] pixel coordinates and reuse category_id for the same object class across references.
  • Treat visual embedding files as base64 text strings expected by TRex2APIWrapper.embedding_inference, not as model checkpoints.
  • Keep raw detection JSON unfiltered when possible; filter only for visualization unless the user asks to discard low-score detections.
  • Convert scores, labels, and boxes to NumPy arrays before calling trex.visualize directly.

Non-goals and avoid-when

  • Do not use this skill for training T-Rex, DINOv, GroundingDINO, YOLO, SAM, or other local detection models.
  • Do not use it when the user needs a fully offline detector with no cloud API token.
  • Do not tell future agents to open or run original repo examples or docs. The needed example behavior is distilled into this skill's references and bundled scripts.
  • Do not import or export this skill from Creator mode unless a separate verification/import task explicitly asks for it; this run was created with a no-import policy.
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