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name: vision-and-generative description: "Use Libra for image classification, GANs, read-mode inspection, pretrained/custom CNN export, and feature-map/debug workflows on image datasets." disable-model-invocation: true metadata: disco-role: operating license: MIT

Vision and Generative Workflows with Libra

Load this sub-skill when a task uses Libra image workflows: CNN classification, image read-mode selection, image preprocessing, pretrained/custom CNN architectures, feature-map visualization, TensorFlowJS/TFLite export, or DCGAN image generation.

What this sub-skill owns

• convolutional_query(...) for image classification
• gan_query(...) for DCGAN generation from a single class of images
• image data layouts: setwise, classwise, csvwise, and already-processed directories
• pretrained, custom_arch, show_feature_map, save_as_tfjs, and save_as_tflite constraints
• image preprocessing side effects such as proc_training_set, proc_testing_set, tfjsmodel, model.tflite, and generated_images

Trigger phrases

Use this route when a user asks to:

• train a CNN from folders of images
• decide which read_mode or image_column to pass
• use VGG, ResNet, MobileNet, DenseNet, or a custom Keras JSON architecture
• export a trained image classifier to TFJS/TFLite
• view feature maps after CNN training
• generate images with Libra's GAN path
• diagnose image folder/class count/path issues

Bundled references

• references/api-reference.md for method parameters and model keys
• references/workflows.md for image classification and GAN recipes
• references/data-formats.md for image dataset layouts
• references/export-and-preprocessing.md for preprocessing/export side effects
• references/troubleshooting.md for read-mode, pretrained/custom architecture, GPU, and TFJS failures

Bundled scripts

• scripts/inspect_image_dataset.py inspects a folder or CSV and suggests a safe read_mode/image_column direction.
• scripts/smoke_cnn_layout.py creates a tiny synthetic image tree and verifies that the layout helper sees a classwise dataset. It does not train a CNN.

Operating notes

1. Treat image training as potentially expensive. Start with layout inspection and a tiny epoch count before full runs.
2. custom_arch requires preprocess=False; the source raises if both custom_arch and preprocessing are requested.
3. Pretrained models require 224x224 inputs when using ImageNet weights.
4. save_as_tfjs=True writes tfjsmodel; save_as_tflite=True writes model.tflite in the current working directory.
5. GAN output is written under generated_images relative to the image data path in the inspected code.
6. Image captioning uses image files but is routed through sub-skills/nlp-and-generation because the public client methods live there. Use this sub-skill only for image path/layout debugging around captioning.

Cross-links

• Use the root skill for install/import compatibility and TensorFlowJS/JAX version notes.
• Route text-only generation and image captioning model calls to sub-skills/nlp-and-generation.
• Route tabular analyze(), recommendation, and dashboard tasks to sub-skills/tabular-modeling unless the model key is convolutional_NN.