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name: imgclsmob description: "Guide imgclsmob image-classification model selection, CPU inference, dataset-aware training and evaluation plans, checkpoint conversion, and bounded multi-framework compatibility workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

imgclsmob

Use this repo-specific skill when a task mentions imgclsmob, gluoncv2, pytorchcv, MXNet/Gluon model providers, ImageNet/CIFAR/SVHN/CUB classification workflows, checkpoint conversion, or the repository's multi-framework training/evaluation conventions.

Route by task

• Model construction, one-batch prediction, preprocessing, checkpoints, or model statistics: read model-inference. Start with the bundled CPU/non-pretrained smoke scripts.
• Dataset roots, metainfo, classification train/eval command plans, metrics, resume state, or batch/device settings: read training-evaluation. Run its filesystem-only preflight before constructing a real command.
• Gluon/MXNet, PyTorch, TF1, TF2, Keras, Chainer, or TFLite checkpoint translation: read conversion. Use its standard-library argument inspector before any backend or file operation.
• TensorFlow 1/2, Tensorpack, Keras/Keras-MXNet, Chainer, or CUDA-specific compatibility: read framework-compatibility. Treat these as bounded-unverified unless a matching backend is independently prepared and exercised.

Read troubleshooting for cross-cutting import, optional-dependency, checkpoint, data, CLI, and device failures. Read repo-provenance before deciding whether a refresh is needed; the skill is anchored to one published repository snapshot.

Verified core and safe defaults

The verified core is CPU MXNet/Gluon plus CPU PyTorch with the external pytorchcv provider. The core smoke contract is: pretrained=False, no network, one synthetic or local image, an NCHW (1, 3, 224, 224) input, and a rank-2 classification output. Use scripts/check_environment.py to report which optional imports are available; it never installs packages or downloads weights.

Install only the packages needed by the selected route. For the verified CPU core, use a NumPy version below 2 with MXNet 1.9.1, a CPU-capable MXNet build, and compatible CPU PyTorch/torchvision wheels plus the external pytorchcv package. Install gluoncv2 at the snapshot version when using its public provider. Do not install the repository's broad legacy requirements file wholesale. Pretrained flags, absent CIFAR/SVHN caches, and publication helpers may contact the network or write artifacts; keep them opt-in.

A successful CPU probe does not prove CUDA, TensorFlow, Keras, Chainer, or legacy Tensorpack behavior. Do not use a visible GPU or a CPU wheel as backend verification. The conversion sub-skill records the GPU-only native conversion limits and the exact TF1/TF2 labels.

Self-contained helper entry points

Run these from the generated skill directory or use their absolute path in an agent's skill installation:

python scripts/check_environment.py --help
python scripts/check_environment.py
python sub-skills/model-inference/scripts/infer_gluon.py --help
python sub-skills/model-inference/scripts/infer_pytorch.py --help
python sub-skills/training-evaluation/scripts/check_dataset_layout.py --help
python sub-skills/training-evaluation/scripts/build_command.py --help
python sub-skills/conversion/scripts/inspect_conversion_args.py --list

The inference helpers default to random-weight CPU smoke tests. The dataset checker performs no framework import or download. The command builder and conversion inspector only validate/print plans; neither launches training or conversion.