---

name: ssd-keras description: "Router for SSD300, SSD512, and SSD7 Keras detection workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

SSD Keras

This skill routes work on the SSD Keras repository. It covers the SSD300, SSD512, and SSD7 model builders, the custom layers and losses they depend on, the data generator and augmentation stack, training and fine-tuning workflows, and Pascal VOC / MS COCO inference and evaluation.

Quick compatibility note

Use a Python 3.7 environment with TensorFlow 1.15.x and Keras 2.2.x. The verified baseline also needs protobuf 3.20.x or lower. The codebase is TensorFlow-only; Theano and CNTK are unsupported. Start with scripts/check_env.py before deeper work.

Choose a route

data-preparation

Use this route when the task is about:

• parsing CSV, Pascal VOC XML, COCO JSON, or HDF5 data
• validating boxes, labels, and image sets
• creating batches or augmentation pipelines
• using DataGenerator, BoxFilter, ImageValidator, or apply_inverse_transforms

Read:

• sub-skills/data-preparation/SKILL.md
• references/data-formats.md
• references/workflows.md
• sub-skills/data-preparation/references/troubleshooting.md

training

Use this route when the task is about:

• building a trainable model
• loading VGG / pretrained SSD weights
• compiling with SSDLoss
• wiring SSDInputEncoder
• fitting on VOC, custom CSV data, or synthetic fixtures
• adapting weights with sample_tensors

Read:

• sub-skills/training/SKILL.md
• references/model-architecture.md
• references/workflows.md
• sub-skills/training/references/troubleshooting.md

inference-evaluation

Use this route when the task is about:

• loading a trained model
• decoding predictions
• drawing or interpreting detections
• computing VOC mAP or COCO JSON output
• using Evaluator, decode_detections, decode_detections_fast, or predict_all_to_json

Read:

• sub-skills/inference-evaluation/SKILL.md
• references/model-architecture.md
• references/workflows.md
• sub-skills/inference-evaluation/references/troubleshooting.md

Shared references

• references/model-architecture.md — constructors, modes, custom layers, encoder/decoder, and box math.
• references/data-formats.md — CSV/XML/JSON/HDF5 layouts and generator outputs.
• references/compatibility.md — supported Python and dependency baseline.
• references/runtime-source.md — bundled runtime module layout used by the smoke scripts.
• references/troubleshooting.md — cross-cutting backend, import, and shape mismatches.
• references/workflows.md — notebook-derived end-to-end recipes.

Smoke path

When you only need a fast confidence check, run:

1. scripts/check_env.py
2. the smoke script owned by the relevant sub-skill
3. the smallest synthetic case described in that sub-skill's troubleshooting notes

The smoke scripts resolve imports from the bundled runtime-src/ copy of the Python modules, so they do not rely on an external checkout.

What this skill does not do

• It does not run or document maintainer release automation.
• It does not expose Theano or CNTK paths.
• It does not depend on the original repository's absolute checkout path.
• It does not require the notebooks to stay available once the skill is loaded.

Entry-point summary

• Bundled runtime source lives under runtime-src/ and mirrors the repo's Python package layout.
• Model constructors live under models/.
• Loss and encoder utilities live under keras_loss_function/ and ssd_encoder_decoder/.
• Dataset parsing and augmentation live under data_generator/.
• Evaluation helpers live under eval_utils/.
• Weight sampling utilities live under misc_utils/.

If you need an unfamiliar object or function, open the shared reference that matches the workflow before reading the source tree.