---

name: mxnet-model-zoo description: "Choose, instantiate, customize, and smoke-check MXNet GluonCV model-zoo models." disable-model-invocation: true metadata: disco-role: operating parent-skill: gluon-cv source-framework: mxnet package: gluoncv license: Apache 2.0

MXNet GluonCV model zoo workflows

Use this sub-skill when the task mentions GluonCV's MXNet model zoo, gluoncv.model_zoo.get_model, pretrained MXNet models, model lists, MXNet inference, reset_class, SSD/YOLO/Faster R-CNN/Mask R-CNN/CenterNet, semantic or instance segmentation, pose, MXNet action recognition, depth, tracking, quantized/int8 variants, or MXNet import/version failures.

Verified operating facts:

• gluoncv.model_zoo.get_model(name, **kwargs) constructs MXNet GluonCV models by registry name.
• gluoncv.model_zoo.get_model_list() returns the MXNet registry; the verified registry contains 332 names.
• A CPU smoke check with get_model('cifar_resnet20_v1', pretrained=False), initialized manually, returned output shape (1, 10) for input (1, 3, 32, 32).
• pretrained accepts False, True, or a string hash selector. True or a hash may download weights into the MXNet model cache unless already present.

Route first

Stay in this sub-skill for MXNet model-name selection, dry-run instantiation, pretrained inference recipes, output-shape checks, custom detector class heads, reset_class, model cache issues, and MXNet-specific compatibility problems.

Route elsewhere when the user's goal is not MXNet model-zoo API work:

• Dataset classes, transforms, presets, loaders, metrics, and annotation validation: ../data-transforms-datasets/
• PyTorch action-recognition, DirectPose, COOT, Torch configs, and DDP: ../torch-video-workflows/
• Full training/evaluation/demo command construction from source scripts: ../training-evaluation-scripts/
• AutoGluon, export, deployment, ONNX/TVM, or programmatic export checks: ../automl-deployment-export/

Default workflow

1. Confirm the environment can import MXNet-backed GluonCV. If not, use references/troubleshooting.md before changing code.

2. Choose a model family and registry name with references/model-zoo-workflows.md or the bundled helper:

   python scripts/mxnet_model_inventory.py --filter yolo3 --names
   

3. For a no-network sanity check, instantiate with pretrained=False; initialize parameters before the first forward:

   import mxnet as mx
   from gluoncv.model_zoo import get_model
   
   ctx = mx.cpu()
   net = get_model('cifar_resnet20_v1', pretrained=False)
   net.initialize(ctx=ctx)
   y = net(mx.nd.random.uniform(shape=(1, 3, 32, 32), ctx=ctx))
   assert tuple(y.shape) == (1, 10)
   

4. Use pretrained=True or pretrained='<hash>' only when network access or an existing model cache is acceptable.

5. For detector class changes, prefer a pretrained detector plus reset_class(classes, reuse_weights=...) when the new labels overlap the original dataset; otherwise instantiate a _custom model with classes=[...] and train/fine-tune normally.

6. Validate outputs by task family: classification logits (N, classes), detection tuples (ids, scores, bboxes), segmentation logits (N, classes, H, W) or list/tuple outputs, pose heatmaps, action logits, depth prediction dictionaries, and tracking tracker outputs.

References

• references/model-zoo-workflows.md — model-name families and recipes for inventory, dry-run, pretrained inference, reset-class, and export-adjacent decisions.
• references/api-reference.md — verified signatures, kwargs, return conventions, and source-backed reset behavior.
• references/troubleshooting.md — import/version/cache/GPU/custom-class/Cython fixes.
• scripts/mxnet_model_inventory.py — safe inventory and tiny CPU dry-forward helper; it performs no downloads.

Source notes

This sub-skill distills public API and behavior from setup.py, README.md, docs/model_zoo, docs/tutorials/index.rst, gluoncv/model_zoo/model_zoo.py, tests/model_zoo/test_model_zoo.py, and the source script families under scripts/classification, scripts/detection, scripts/instance, scripts/pose, scripts/segmentation, scripts/action-recognition, scripts/depth, and scripts/tracking. Long demo/train/eval scripts are treated as reference evidence because they require external images/videos/datasets, pretrained downloads, GPUs, or long-running jobs.