---

name: vision-workflows description: "Routes AXLearn vision model configs, ImageNet inputs, and ResNet/CLIP-style recipes." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

vision-workflows

Use this sub-skill for AXLearn's image-centric workflows.

Typical triggers:

• ImageNet, ResNet, ImageClassificationModel, or ImagenetInput.
• CLIP, CoCa, CyCLIP, or other vision-language model helpers.
• Image preprocessing, fake image datasets, crop/augment/whiten helpers, or vision trainer configs.

If the task is only about shared trainer plumbing, use ../training-core/ first. If the task is about axlearn gcp ..., use ../cli-cloud/. If the task is about ASR, use ../audio-asr/.

What to read

• references/workflows.md for ImageNet and model-builder workflows.
• references/troubleshooting.md for fake-data, dataset, and shape issues.
• scripts/inspect_vision_configs.py for a safe config-inspection helper.

Common routes

Inspect a ResNet trainer catalog

python scripts/inspect_vision_configs.py --module axlearn.experiments.vision.resnet.imagenet_trainer --config ResNet-Test

Run a CPU-safe fake-data probe

Use DATA_DIR=FAKE so the ImageNet helpers switch to synthetic inputs.

Inspect the image-classification model API

The central model is ImageClassificationModel, which wraps a backbone plus classifier head. The ResNet family provides backbone configs such as resnet18_config() and resnet50_config().

Decision points

• Choose this sub-skill when the user names a specific image-classification model or ImageNet recipe.
• Keep shared trainer mechanics in training-core.
• Do not route speech or GPT catalogs here just because they also use SpmdTrainer.