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name: image-modeling description: "Routes easy12306 image-tile classifier training, asset inspection, and model artifact compatibility tasks." disable-model-invocation: true metadata: disco-role: operating license: Artistic 2.0

image-modeling

Use this sub-skill when a task is about the easy12306 image-tile classifier: training or inspecting the 80-way model that labels each cropped captcha tile, checking captcha.npz / captcha.test.npz, understanding BGR preprocessing, or preparing the 12306.image.model.h5 artifact for downstream inference.

This is a router. Keep detailed architecture, data schemas, and failure handling in the linked references instead of expanding this file.

Route here for

• Explaining or recreating the image classifier training pipeline.
• Validating image-training datasets before a long run.
• Auditing why class vote matrices produce per-sample weights.
• Checking whether a proposed 12306.image.model.h5 handoff has the expected 80-class purpose and compatible environment assumptions.
• Diagnosing Keras/TensorFlow compatibility for the legacy image-training flow.

Route elsewhere

• Text prompt recognition, model.h5, model.v*.h5, or text .npz datasets: use the text-modeling sub-skill.
• Captcha download, crop/tile generation, perceptual hashes, OCR-assisted labeling, or images.npz: use the data-preparation sub-skill.
• Quick pretrained prediction, end-to-end captcha output interpretation, or grid-position answers from existing model artifacts: use the inference sub-skill.

Read next

• references/workflows.md for end-to-end asset inspection, safe retraining planning, and model handoff steps.
• references/api-reference.md for the distilled preprocess_input, load_data, learn, predict, and _predict contracts.
• references/troubleshooting.md for Keras 3, missing data/model files, sample-weight anomalies, VGG16 downloads, and BGR/RGB mistakes.
• scripts/inspect_image_training_assets.py to validate .npz dataset shape, 80-label vocabulary files, optional model existence, and vote-matrix sample weights without loading TensorFlow by default.

When the integrated root files are present, also read ../../references/label-vocabulary.md for class-id-to-label mapping and ../../references/model-artifacts.md for shared model file placement and handoff conventions.

Safe first step

Before any training or artifact handoff, run the bundled asset checker against user-supplied files. From this sub-skill directory, for example:

python scripts/inspect_image_training_assets.py \
  --captcha-npz captcha.npz \
  --captcha-test-npz captcha.test.npz \
  --labels-file texts.txt \
  --model 12306.image.model.h5

The checker validates array schemas and file presence only. It intentionally does not load a Keras model unless --load-model is supplied.

Operating contract

• Image arrays are OpenCV-style BGR tiles. The classifier preprocessing converts images to float32 and subtracts BGR means [103.939, 116.779, 123.68].
• The training labels are either sparse class ids or an 80-column vote/probability matrix. Vote matrices drive the legacy sample-weight formula documented in references/api-reference.md.
• The trained artifact is 12306.image.model.h5, a softmax-80 classifier for captcha image tiles. It is not bundled here; future agents must receive or create it explicitly.
• The legacy training recipe uses ImageNet VGG16 weights, augmentation, 400 epochs, and 100 generator steps per epoch. Treat it as expensive and possibly network-dependent, never as a smoke test.
• Python 3.11 with Keras/TensorFlow 2.15 was verified for inspection. Keras 3 removes the legacy keras.preprocessing.image.ImageDataGenerator import path used by this workflow.