---

name: gluon-cv description: "Use GluonCV for computer-vision model-zoo, dataset, transform, training-script, AutoML, and deployment workflows across MXNet and PyTorch backends." disable-model-invocation: true metadata: disco-role: operating package: gluoncv version: "0.11.0" license: Apache 2.0

GluonCV repo skill

Use this skill when a task names GluonCV, gluoncv, MXNet GluonCV, the GluonCV model zoo, GluonCV Torch action-recognition models, GluonCV datasets/transforms, GluonCV script-zoo training/evaluation commands, gluoncv.auto, or GluonCV export/deployment workflows.

GluonCV 0.11.0 is a legacy computer-vision toolkit with two backend families:

• MXNet: broad model zoo, datasets, transforms, metrics, and most classic training/eval/demo scripts.
• PyTorch: concentrated support for action recognition/video, DirectPose, COOT/video-language, Torch configs, and DDP helpers.

First checks

1. Verify installation and backend compatibility before using APIs:

   python scripts/check_gluoncv_environment.py
   

2. If import fails, read references/install-and-backends.md and references/troubleshooting.md before changing code.

3. Choose a sub-skill by the user's task shape, not just by source directory names.

Route map

User task	Read
Pick, list, instantiate, smoke-check, or customize MXNet GluonCV models; use gluoncv.model_zoo.get_model; handle reset_class for SSD/YOLO/Faster R-CNN/Mask R-CNN; diagnose MXNet model-zoo failures.	sub-skills/mxnet-model-zoo/SKILL.md
Prepare/validate datasets, bbox records, image/video transforms, batchify functions, dataset roots, metrics, visualization, or dataset-preparation script expectations.	sub-skills/data-transforms-datasets/SKILL.md
Use PyTorch GluonCV action-recognition/video models, DirectPose, COOT, YACS configs, CPU smokes, DDP helpers, tensor shapes, or Torch optional dependency fixes.	sub-skills/torch-video-workflows/SKILL.md
Build safe command templates from GluonCV's classification/detection/segmentation/pose/action/depth/tracking/GAN/Re-ID training/evaluation/demo script zoo.	sub-skills/training-evaluation-scripts/SKILL.md
Use optional gluoncv.auto AutoGluon tasks, validate export/deployment model names, or reason about ONNX/TVM/quantized/int8 deployment prerequisites.	sub-skills/automl-deployment-export/SKILL.md

Backend and install guidance

Read references/install-and-backends.md when choosing dependencies. Important legacy constraints:

• gluoncv requires at least one backend import to succeed: MXNet >=1.4,<2.0 or PyTorch >=1.4,<2.0.
• Modern default packages can be incompatible. MXNet 1.9.x commonly needs numpy<1.24; the Torch side may need Pillow<10 because legacy code references PIL.Image.LINEAR.
• gluoncv[full] and gluoncv[auto] pull optional old stacks. Install only the extras needed for the selected workflow.
• CUDA, DDP, DALI, Horovod, TVM, ONNX, decord, pycocotools, and AutoGluon are optional workflow dependencies unless the user explicitly selects them.

Minimal import check:

import gluoncv
print(gluoncv.__version__)
print(getattr(gluoncv, '_found_mxnet', None), getattr(gluoncv, '_found_pytorch', None))

If both MXNet and PyTorch are installed, GluonCV may warn about increased GPU memory footprint. Treat that warning as expected unless the task is memory-sensitive.

Source and freshness

• Read references/repo-provenance.md before deciding whether this skill matches a current checkout or should be refreshed.
• Runtime content here is self-contained. Do not rely on the original repository checkout to find examples, scripts, docs, tests, or configs; use the bundled references and helpers instead.
• If the user is editing GluonCV's repository internals rather than using the package, still start here for public API context, then use repository-maintenance practices and focused native tests appropriate to the edit.

Safety defaults

• Prefer pretrained=False, CPU smokes, registry/list checks, and helper --help or JSON validation before network, GPU, training, benchmark, export, or dataset-conversion side effects.
• Confirm data paths, output directories, accelerator availability, runtime budget, network/cache policy, and overwrite behavior before launching real jobs.
• Keep optional backend claims explicit: CPU API smokes do not verify CUDA performance, DDP behavior, TVM compilation, ONNX runtime, or AutoGluon training.