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---
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
```bash
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`.
