---

name: tf-unet description: "Build, train, and inspect tf_unet TensorFlow 1.x image-segmentation workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

tf_unet

Use this skill when the task names tf_unet, tf-unet, TensorFlow U-Net, image segmentation with this package, or the bundled toy/demo launchers.

This is a legacy TensorFlow 1.x package. Keep tf.Session, tf.placeholder, and tf.reset_default_graph in mind. Read references/installation-and-compatibility.md before changing environments.

Quick start

1. Run scripts/check_tf_unet_env.py to confirm the install, TensorFlow 1.x import, and a tiny model build.
2. Read references/troubleshooting.md when import errors, protobuf mismatches, or shape problems show up.
3. Route to a sub-skill:
   • sub-skills/training-and-inference/SKILL.md for Unet, Trainer, losses, save/restore, prediction, and visualization.
   • sub-skills/data-providers-and-launchers/SKILL.md for BaseDataProvider, ImageDataProvider, GrayScaleDataProvider, RgbDataProvider, and launcher notebooks/scripts.
4. Use references/repo-provenance.md to compare this skill with the source snapshot.

Installation

A compatible baseline is Python 3.7 plus TensorFlow 1.15.5 and protobuf 3.20.3. Add click, Pillow, matplotlib, scipy, and h5py when you need the launcher workflows. Install the distribution that exposes tf_unet in your environment, then run the root smoke helper.

python -m pip install tensorflow==1.15.5 protobuf==3.20.3 click Pillow matplotlib scipy h5py
python scripts/check_tf_unet_env.py

What this skill covers

• Toy circle segmentation and generic image-segmentation experiments.
• Checkpoint creation, restore, prediction, and output image composition.
• TIFF/HDF5/NumPy data-provider patterns and the bundled launcher workflows.
• Safe inspection only; do not assume the original checkout is available at runtime.

Routing cues

• A request about the network graph, optimizer, loss, or prediction output belongs in training-and-inference.
• A request about image files, one-hot labels, HDF5 chunks, or dataset-specific launcher patterns belongs in data-providers-and-launchers.
• A request about install or runtime compatibility belongs in the root references.
• A request about docs/build automation or binder bootstrap is out of scope unless it is needed to explain a user-facing workflow.

Bundled references

• references/installation-and-compatibility.md — read for package/dependency versions, Python guidance, and the protobuf pin that keeps TF 1.15.x importable.
• references/troubleshooting.md — read for TensorFlow 1.x, shape, file layout, and launcher pitfalls.
• references/repo-provenance.md — read to compare this skill with the source snapshot.
• references/repo-routing-metadata.json — consumed by the repo-skills router during import.

Bundled scripts

• scripts/check_tf_unet_env.py — run when you need a tiny import/build/save/restore smoke before touching a workflow.
• sub-skills/training-and-inference/scripts/smoke_train_restore.py — run when you need a tiny training/predict smoke tied to the model graph.
• sub-skills/data-providers-and-launchers/scripts/smoke_data_providers.py — run when you need to validate provider shapes and image/mask pairing on synthetic fixtures.

Common safety notes

• Keep training smoke tiny; this package was validated with a small synthetic model, not a long training job.
• Do not tell future agents to run original repo notebooks or scripts from the source checkout. If a workflow matters, use the bundled references and scripts in this skill tree.
• Treat external datasets as optional evidence, not as runtime dependencies.